{"id":38,"date":"2024-04-03T23:09:24","date_gmt":"2024-04-03T20:09:24","guid":{"rendered":"https:\/\/sisu.ut.ee\/measurement\/6-random-and-systematic-effects-revisited\/"},"modified":"2025-03-11T13:05:00","modified_gmt":"2025-03-11T11:05:00","slug":"6-random-and-systematic-effects-revisited","status":"publish","type":"page","link":"https:\/\/sisu.ut.ee\/measurement\/6-random-and-systematic-effects-revisited\/","title":{"rendered":"6. Random and systematic effects revisited"},"content":{"rendered":"<p><strong>Brief summary:\u00a0<\/strong>This section explains that whether an effect will influence the measurement result as a random or as a systematic effect depends on the conditions. Effects that are systematic in short term can become random in long term. This is the reason why repeatability is by its value smaller than within-lab reproducibility and the latter is in turn smaller than the combined standard uncertainty. This section also explains that the A and B type uncertainty estimates do not correspond one to one to the random and systematic effects.<\/p>\n<p style=\"text-align: center;\"><\/p><div class=\"ratio ratio-16x9 mb-3\"><div class=\"video-placeholder-wrapper video-placeholder-wrapper--16x9\">\n\t\t\t    <div class=\"video-placeholder d-flex justify-content-center align-items-center\">\n\t\t\t        <div class=\"overlay text-white p-2 w-100 text-center d-block justify-content-center align-items-center\">\n\t\t\t            <div>To view third-party content, please accept cookies.<\/div>\n\t\t\t            <button class=\"btn btn-secondary btn-sm mt-1 consent-change\">Change consent<\/button>\n\t\t\t        <\/div>\n\t\t\t    <\/div>\n\t\t\t<\/div>\n<\/div>\n<h4 style=\"text-align: center;\"><strong>How a within-day systematic effect can become a long-term random effect?<\/strong>\u00a0<br><a style=\"line-height: 1.6em;\" href=\"http:\/\/www.uttv.ee\/naita?id=17713\" target=\"_blank\" rel=\"noopener\">http:\/\/www.uttv.ee\/naita?id=17713<\/a><\/h4>\n<p style=\"text-align: center;\"><a href=\"https:\/\/www.youtube.com\/watch?v=qObLSS7mfDo\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/watch?v=qObLSS7mfDo<\/a><\/p>\n<p><strong><em>Random and systematic effects in the short term and in the long term<\/em><\/strong><\/p>\n<p>An effect that within a short time period (e.g. within a day) is systematic can over a longer time period be random. Examples:<\/p>\n<ol>\n<li>If a number of pipetting operations are done within a day using the same pipette then the difference of the actual volume of the pipette from its nominal volume (i.e. calibration uncertainty) will be a systematic effect. If pipetting is done on different days and the same pipette is used then it is also a systematic effect. However, if pipetting is done on different days and different pipettes are used then this effect will change into a random effect.<\/li>\n<li>An instrument is calibrated daily with calibration solutions made from the same stock solution, which is remade every month. In this case the difference of the actual stock solution concentration and its nominal concentration is a systematic effect within a day and also within few weeks. But over a longer time period, say, half a year <a href=\"#\" data-bs-toggle=\"modal\" data-bs-target=\"#popup-modal\" data-title=\"[1]\" data-content=\"It cannot be strictly defined, how long is \u201elong-term\u201c. An approximate guidance could be: one year is good, \u201eseveral months\u201c (at least 4-5) is minimum. Of course it also depends on the procedure.\">[1]<\/a>\u00a0this effect becomes random, since a number of different sock solutions will have been in use during that time period.<\/li>\n<\/ol>\n<p>Conclusions:<\/p>\n<ol>\n<li>An effect, which is systematic in short term can be random in long term;<\/li>\n<li>The longer is the time frame the more effects can change from systematic into random.<\/li>\n<\/ol>\n<p>As explained in a past lecture if the measurement of the same or identical sample is repeated under identical conditions (usually within the same day) using the same procedure then the standard deviation of the obtained results is called <strong style=\"line-height: 1.6em;\">repeatability standard deviation<\/strong> and denoted as <strong style=\"line-height: 1.6em;\"><em>s<\/em><sub>r<\/sub><\/strong>. If the measurement of the same or identical sample is repeated using the same procedure but under changed conditions whereby the changes are those that take place in the laboratory under normal work practices then the standard deviation of the results is called <strong style=\"line-height: 1.6em;\">within-lab reproducibility<\/strong> or <strong style=\"line-height: 1.6em;\">intermediate precision<\/strong> and it is denoted as <strong style=\"line-height: 1.6em;\"><em>s<\/em><sub>RW<\/sub><\/strong>.\u00a0<a href=\"#\" data-bs-toggle=\"modal\" data-bs-target=\"#popup-modal\" data-title=\"[2]\" data-content=\"[2] The terms \u201ewithin-lab reproducibility\u201c and \u201eintermediate precision\u201c are synonyms. The VIM&lt;sup&gt;(1)&lt;\/sup&gt; prefers intermediate precision. The Nordtest handbook&lt;sup&gt;(5)&lt;\/sup&gt; uses within-lab reproducibility (or reproducibility within laboratory). In order to stress the importance of the \u201elong-term\u201c, in this course we often refer to s&lt;sub&gt;RW&lt;\/sub&gt; as the within-lab long-term reproducibility. \">[2]<\/a><\/p>\n<p>The conclusions expressed above are the reason why <em style=\"line-height: 1.6em;\">s<\/em><sub>r<\/sub> is smaller than <em style=\"line-height: 1.6em;\">s<\/em><sub>RW<\/sub>. Simply, some effects that within day are systematic and are not accounted for by <em style=\"line-height: 1.6em;\">s<\/em><sub>r<\/sub> become random over a longer time and <em style=\"line-height: 1.6em;\">s<\/em><sub>RW<\/sub> takes them into account. The combined standard uncertainty <em style=\"line-height: 1.6em;\">u<\/em><sub>c<\/sub> is in turn larger that the intermediate precision, because it has to take into account all significant effects that influence the result, including those that remain systematic also in the long term. This important relation between these three quantities is visualized in Scheme 6.1.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-129 aligncenter\" style=\"margin-right: auto; margin-left: auto;\" title=\"6-1.png\" src=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6-1.png\" alt=\"6-1.png\" width=\"590\" height=\"115\" srcset=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6-1.png 672w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6-1-300x58.png 300w\" sizes=\"auto, (max-width: 590px) 100vw, 590px\"><\/p>\n<p style=\"text-align: center;\"><strong>Scheme 6.1. Relations between repeatability, within-lab reproducibility and combined uncertainty.<\/strong><\/p>\n<p style=\"text-align: left;\">The random and systematic effects cannot be considered to be in one-to-one relation with type A and B uncertainty estimation. These are categorically different things. The effects refer to the intrinsic causal relationships, while type A and B uncertainty estimation refers rather to approaches used for quantifying uncertainty. Table 6 illustrates this further.<\/p>\n<p style=\"text-align: center;\"><strong>Table 6.1. Interrelations between random and systematic effects and A and B types of uncertainty estimates.<\/strong><\/p>\n<table class=\"table table-hover\" style=\"width: 100%; border: 1px solid #000000; border-style: solid;\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"center\">\n<tbody>\n<tr>\n<td style=\"text-align: center; border: 1px solid #000000;\" valign=\"top\">\n<p><strong>Effect<\/strong><\/p>\n<\/td>\n<td style=\"text-align: center; border: 1px solid #000000;\" valign=\"top\">\n<p><strong>Type A estimation<\/strong><\/p>\n<\/td>\n<td style=\"text-align: center; border: 1px solid #000000;\" valign=\"top\">\n<p><strong>Type B estimation<\/strong><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" valign=\"top\">\n<p>Random<\/p>\n<\/td>\n<td style=\"border: 1px solid #000000;\" valign=\"top\">\n<p>The usual way of estimating uncertainties caused by random effects<\/p>\n<\/td>\n<td style=\"border: 1px solid #000000;\" valign=\"top\">\n<p>Type B estimation of the uncertainty caused by random effects is possible if no repeated measurements are carried out and the data\/information on the magnitude of the effect is instead available from different sources.<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" valign=\"top\">\n<p>Systematic<\/p>\n<\/td>\n<td style=\"border: 1px solid #000000;\" valign=\"top\">\n<p>This is only possible if the effect will change into a random effect in the long term<\/p>\n<\/td>\n<td style=\"border: 1px solid #000000;\" valign=\"top\">\n<p>The usual way of estimating uncertainties caused by the systematic effects<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>So, depending on the timeline, all the effects causing uncertainty can be grouped as pictured in Scheme 6.2. In the short-term view, most effects act as systematic and the random effects can be quantified via repeatability. In the long term more (usually most) effects are random and can be quantified via within-lab reproducibility (intermediate precision).<\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-131\" title=\"6_sheme.6.2_02.png\" src=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6_sheme.6.2_02.png\" alt=\"6_sheme.6.2_02.png\" width=\"799\" height=\"400\" srcset=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6_sheme.6.2_02.png 1460w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6_sheme.6.2_02-300x150.png 300w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6_sheme.6.2_02-1024x513.png 1024w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/6_sheme.6.2_02-768x385.png 768w\" sizes=\"auto, (max-width: 799px) 100vw, 799px\"><\/p>\n<p style=\"text-align: center;\"><strong>Scheme 6.2. Two ways of grouping effects that cause uncertainty (short-term and long-term).<\/strong><\/p>\n<p>As is explained in section 8, the two main uncertainty estimation approaches addressed in this course use the above ways as follows: The modelling (ISO GUM) approach tends to follow to the short-term view (estimating uncertainty of one concrete result on one concrete day), while the Single-lab approach (Nordtest) always follows the long-term view (estimating an average uncertainty of the procedure). See sections 8-11 for more information.<\/p>\n<p><strong><em>Determining repeatability and within-lab reproducibility in practice<\/em><\/strong><\/p>\n<p>The typical requirements for determining <em>s<\/em><sub>r<\/sub> and <em>s<\/em><sub>RW<\/sub> are presented in Table 6.2.<\/p>\n<p style=\"text-align: center;\"><strong>Table 6.2. Typical requirements\u00a0<\/strong><strong>for determining\u00a0<em>s<\/em>r\u00a0and\u00a0<em>s<\/em>RW\u00a0of an analytical procedure.<\/strong><\/p>\n<table class=\"table table-hover\" style=\"border: 1px solid #000000;\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<tbody>\n<tr>\n<td style=\"border: 1px solid #000000;\" align=\"center\" valign=\"middle\"><strong>Repeatability\u00a0<span class=\"font7\">s<\/span><sub><span class=\"font8\">r<\/span><\/sub><\/strong><\/td>\n<td style=\"border: 1px solid #000000;\" align=\"center\" valign=\"middle\"><strong>Within-lab reproducibility\u00a0<span class=\"font7\">s<\/span><sub><span class=\"font8\">RW<\/span><\/sub><\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" colspan=\"2\" align=\"center\" valign=\"middle\">There is a\u00a0<strong><span class=\"font6\">sufficient amount<\/span><\/strong><span class=\"font5\">\u00a0of a\u00a0<\/span><strong><span class=\"font6\">stable<\/span><\/strong><span class=\"font5\">\u00a0and\u00a0<\/span><strong><span class=\"font6\">homogeneous<\/span><\/strong><span class=\"font5\">\u00a0sample (control sample)<\/span><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" colspan=\"2\" align=\"center\" valign=\"middle\">The control sample has to be\u00a0<strong><span class=\"font6\">similar to the routinely analysed samples<\/span><\/strong><span class=\"font5\"><strong>\u00a0<\/strong>by analyte content and by\u00a0<\/span><strong><span class=\"font6\">difficulty level<\/span><\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" align=\"center\" valign=\"middle\">Sample has to be stable within a day<\/td>\n<td style=\"border: 1px solid #000000;\" align=\"center\" valign=\"middle\">Sample has to be\u00a0<strong><span class=\"font6\">stable for months<\/span><\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" align=\"center\" valign=\"middle\">Measurements with subsamples of the control sample are carried out on the same day under the same conditions<\/td>\n<td style=\"border: 1px solid #000000;\" align=\"center\" valign=\"middle\">On days when the analysis procedure is used, in addition to calibrants and customer samples also a subsample of the control sample is analysed<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" colspan=\"2\" align=\"center\" valign=\"middle\">The subsample of the control sample has to<strong>\u00a0<span class=\"font6\">through all the steps of the procedure<\/span><\/strong><span class=\"font5\">, including the sample preparation steps<\/span><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #000000;\" colspan=\"2\" align=\"center\" valign=\"middle\">s<sub><span class=\"font10\">r<\/span><\/sub><span class=\"font5\">\u00a0or\u00a0<\/span><span class=\"font9\">s<\/span><sub><span class=\"font10\">RW<\/span><\/sub><span class=\"font5\">\u00a0is found as standard deviation of the results obtained with subsamples of the control sample<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>When estimating the uncertainty contributions due to random effects, then it is important that a number of repeated measurements are carried out. On the other hand, if, e.g. repeatability of some analytical procedure is estimated then each repetition has to cover all steps in the procedure, including sample preparation. For this reason making extensive repetitions is very work-intensive. In this situation the concept of <strong>pooled standard deviation<\/strong> becomes very useful. Its essence is pooling standard deviations obtained from a limited number of measurements. The following video explains this:<\/p>\n<p style=\"text-align: center;\"><\/p><div class=\"ratio ratio-16x9 mb-3\"><div class=\"video-placeholder-wrapper video-placeholder-wrapper--16x9\">\n\t\t\t    <div class=\"video-placeholder d-flex justify-content-center align-items-center\">\n\t\t\t        <div class=\"overlay text-white p-2 w-100 text-center d-block justify-content-center align-items-center\">\n\t\t\t            <div>To view third-party content, please accept cookies.<\/div>\n\t\t\t            <button class=\"btn btn-secondary btn-sm mt-1 consent-change\">Change consent<\/button>\n\t\t\t        <\/div>\n\t\t\t    <\/div>\n\t\t\t<\/div>\n<\/div>\n<h4 style=\"text-align: center;\"><strong>Pooled standard deviation<\/strong><br><a href=\"http:\/\/www.uttv.ee\/naita?id=18228\" target=\"_blank\" rel=\"noopener\">http:\/\/www.uttv.ee\/naita?id=18228<\/a><\/h4>\n<p style=\"text-align: center;\"><a href=\"https:\/\/www.youtube.com\/watch?v=xsltS41PZW0\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/watch?v=xsltS41PZW0<\/a><\/p>\n<p style=\"text-align: left;\">Depending on how the experiments are planned, the pooled standard deviation can be used for calculating of either repeatability <em>s<\/em><sub>r<\/sub> or within-lab reproducibility <em>s<\/em><sub>RW<\/sub>. The experimental plan and calculations when finding repeatability <em>s<\/em><sub>r<\/sub> are explained in the following video:<\/p>\n<p style=\"text-align: center;\"><\/p><div class=\"ratio ratio-16x9 mb-3\"><div class=\"video-placeholder-wrapper video-placeholder-wrapper--16x9\">\n\t\t\t    <div class=\"video-placeholder d-flex justify-content-center align-items-center\">\n\t\t\t        <div class=\"overlay text-white p-2 w-100 text-center d-block justify-content-center align-items-center\">\n\t\t\t            <div>To view third-party content, please accept cookies.<\/div>\n\t\t\t            <button class=\"btn btn-secondary btn-sm mt-1 consent-change\">Change consent<\/button>\n\t\t\t        <\/div>\n\t\t\t    <\/div>\n\t\t\t<\/div>\n<\/div>\n<h4 style=\"text-align: center;\"><strong>Pooled standard deviation in practice: estimating repeatability<\/strong> \u00a0<br><a href=\"http:\/\/www.uttv.ee\/naita?id=18232\" target=\"_blank\" rel=\"noopener\">http:\/\/www.uttv.ee\/naita?id=18232<\/a><\/h4>\n<p style=\"text-align: center;\"><a href=\"https:\/\/www.youtube.com\/watch?v=DM_zf85PYic\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/watch?v=DM_zf85PYic<\/a><\/p>\n<p>The experimental plan and calculations when finding within-lab reproducibility <em>s<\/em><sub>RW<\/sub> are explained in the following video:<\/p>\n<p style=\"text-align: center;\"><\/p><div class=\"ratio ratio-16x9 mb-3\"><div class=\"video-placeholder-wrapper video-placeholder-wrapper--16x9\">\n\t\t\t    <div class=\"video-placeholder d-flex justify-content-center align-items-center\">\n\t\t\t        <div class=\"overlay text-white p-2 w-100 text-center d-block justify-content-center align-items-center\">\n\t\t\t            <div>To view third-party content, please accept cookies.<\/div>\n\t\t\t            <button class=\"btn btn-secondary btn-sm mt-1 consent-change\">Change consent<\/button>\n\t\t\t        <\/div>\n\t\t\t    <\/div>\n\t\t\t<\/div>\n<\/div>\u00a0\n<h4 style=\"text-align: center;\"><strong>Pooled standard deviation in practice: estimating within-lab long-term reproducibility<\/strong><br><a href=\"http:\/\/www.uttv.ee\/naita?id=18234\" target=\"_blank\" rel=\"noopener\">http:\/\/www.uttv.ee\/naita?id=18234<\/a><\/h4>\n<p style=\"text-align: center;\"><a href=\"https:\/\/www.youtube.com\/watch?v=nPJY8HfPxNs\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/watch?v=nPJY8HfPxNs<\/a><\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/sisu.ut.ee\/measurement\/self-test-6\/\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-60\" title=\"selftest.png\" src=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/selftest.png\" alt=\"selftest.png\" width=\"104\" height=\"41\"><\/a><\/p>\n<p style=\"text-align: left;\">***<\/p>\n<div>\n<p>[1]\u00a0It cannot be strictly defined, how long is \u201elong-term\u201c. An approximate guidance could be: one year is good, \u201eseveral months\u201c (at least 4-5) is minimum. Of course it also depends on the procedure.<\/p>\n<\/div>\n<div>\n<p>[2]\u00a0The terms \u201ewithin-lab reproducibility\u201c and \u201eintermediate precision\u201c are synonyms. The VIM<sup>(1)<\/sup> prefers intermediate precision. The Nordtest handbook<sup>(5)<\/sup> uses within-lab reproducibility (or reproducibility within laboratory). In order to stress the importance of the \u201elong-term\u201c, in this course we often refer to <em>s<\/em><sub>RW<\/sub> as the within-lab long-term reproducibility.<\/p>\n<p>***<\/p>\n<p>The slides of the presentation and\u00a0the calculation files \u2013 with initial data only, as well, as with calculations performed\u00a0\u2013\u00a0are available from here:\u00a0<\/p>\n<\/div>\n\n\n<div class=\"wp-block-group attached-files-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-86630ddf-6117-45db-a843-8cef94315174\" href=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/pooled_standard_deviation_repeatability_initial.xlsx\" target=\"_blank\" rel=\"noreferrer noopener\">pooled_standard_deviation_repeatability_initial.xlsx<\/a><\/div>\n\n\n\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-d1a825a9-34db-4a7b-9f56-66359960ccd6\" href=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/pooled_standard_deviation_repeatability_solved.xlsx\" target=\"_blank\" rel=\"noreferrer noopener\">pooled_standard_deviation_repeatability_solved.xlsx<\/a><\/div>\n\n\n\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-63741a23-d6fd-4701-aaa6-4413700b5544\" href=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/pooled_standard_deviation_reproducibility_initial.xlsx\" target=\"_blank\" rel=\"noreferrer noopener\">pooled_standard_deviation_reproducibility_initial.xlsx<\/a><\/div>\n\n\n\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-f7b3dbe1-d47f-47f6-becd-536422c51971\" href=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/pooled_standard_deviation_reproducibility_solved.xlsx\" target=\"_blank\" rel=\"noreferrer noopener\">pooled_standard_deviation_reproducibility_solved.xlsx<\/a><\/div>\n\n\n\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-55e5ed2b-5e51-4aa7-b2c1-8370fa132862\" href=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/18\/pooled_standard_deviation.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">pooled_standard_deviation.pdf<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Brief summary:\u00a0This section explains that whether an effect will influence the measurement result as a random or as a systematic effect depends on the conditions. Effects that are systematic in short term can become random in long term. This is &#8230;<\/p>\n","protected":false},"author":14,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-38","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/pages\/38","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/comments?post=38"}],"version-history":[{"count":9,"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/pages\/38\/revisions"}],"predecessor-version":[{"id":845,"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/pages\/38\/revisions\/845"}],"wp:attachment":[{"href":"https:\/\/sisu.ut.ee\/measurement\/wp-json\/wp\/v2\/media?parent=38"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}