Short communication: Repeatability of measures of rectal temperature in dairy cows
Bibliographic record
Abstract
Although taking body temperature by rectal thermometer is the method most commonly used to identify sick cows in the postpartum period, no data on the repeatability of this measure are available. The overall objective of this study was to evaluate variability of rectal temperatures in dairy cows considering different factors (intra- and interinvestigator repeatability, different thermometers, penetration depth into the rectum, and defecation). High coefficients of correlation (r=0.98) and small differences between values of rectal temperatures (observer A=39.3+/-1.0 degrees C and observer B=39.4+/-1.0 degrees C) provide evidence that rectal temperature was a repeatable measure in dairy cows. Testing was carried out using 4 different digital thermometers: GLA M750 (GLA Agricultural Electronics, San Luis Obispo, CA), MTI8101 (SES Scala Electronics, Stahnsdorf, Germany), MT1831 (Microlife AG, Widnau, Switzerland) and Domotherm TH1 (Uebe Medical GmbH, Wertheim, Germany). Thermometers were inserted into the rectum to a certain depth (GLA M750 and MTI8101=11.5cm; MT1831=8.4cm; Domotherm TH1=7.7cm) and a measure was finished when a visual or acoustic signal was emitted by the thermometer. The measures could be influenced by the procedure itself (up to 0.5 degrees C), type of thermometer (up to 0.3 degrees C), and the penetration depth (11.5cm or 6.0cm in one of the experiments) into the rectum (up to 0.4 degrees C difference between a penetration depth of 11.5cm and 6.0cm in one of the experiments). Differences in rectal temperature before and after defecation were minor (<0.1 degrees C). These results indicate that some care is required in generalizing rectal measures of body temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".