Technical note: Comparison of rectal and vaginal temperatures in lactating dairy cows
Bibliographic record
Abstract
A method commonly used to identify illness in dairy cows is measuring body temperatures with a rectal thermometer, but vaginal measures are becoming common in research. The primary objective of this study was to validate vaginal measures of body temperature by comparing them with rectal temperatures. Data loggers used to collect vaginal temperatures can be programmed to collect many readings per day, providing an opportunity to interpret effects of health in relation to diurnal differences in temperatures. Thus, a secondary objective was to compare the diurnal pattern in body temperatures for cows with and without retained placenta (RP). Body temperature was monitored for 8 d in 29 cows that had recently calved (enrolled 2 d after calving; 7 of these cows were diagnosed with RP) and in 13 cows in peak lactation (98±8 d in milk). Rectal temperatures were taken at 0630, 0930, 1230, 1530, 1830, and 2130h (±30 min) with a digital thermometer for 8 d consecutively. During the same period, vaginal temperatures were measured every 10 min with a microprocessor-controlled data logger attached to a modified vaginal controlled internal drug release insert. Values from the vaginal loggers were averaged over 1h and paired with the corresponding rectal temperature. There was a relationship between rectal and vaginal temperatures for fresh cows (n=1,393; r=0.81) and for peak-lactation cows (n=556; r=0.46). Cows with RP had higher body temperatures (39.2±0.01) compared with healthy cows (39.1±0.01). Body temperature was higher at night, and lower between 0800 to 1000 h for healthy cows (39.0±0.02) and between 1100 to 1300 h for RP cows (39.1±0.02). In summary, vaginal temperatures were associated with rectal measures, and provided the advantage of capturing dirurnal changes in body temperature.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".