The collection of multiple saliva samples from pigs and the effect on adrenocortical activity
Bibliographic record
Abstract
Cook, N. J., Hayne, S. M., Rioja-Lang, F. C., Schaefer, A. L. and Gonyou, H. W. 2013. The collection of multiple saliva samples from pigs and the effect on adrenocortical activity. Can. J. Anim. Sci. 93: 329–333. The validity of collecting multiple saliva samples for the measurement of cortisol was tested in two sampling regimes in two weight classes of grower pigs (50 and 100 kg). The sampling regimes were a high-frequency, short-duration (HFSD) protocol involving collection of multiple samples within approximately 2 min of each other over a period 30 min. The second regime was a low-frequency, long-duration (LFLD) protocol in which samples were collected every 30 min for 3 h. Both sampling regimes were applied to individually housed pigs. The effect of repeated sampling of a focal pig on its cohorts in a group-housed pen was tested using the LFLD regime. There was no evidence of an effect of either of the sampling protocols on salivary cortisol concentrations in individually housed or group-housed pigs. There was some evidence that higher concentrations of salivary cortisol were associated with longer individual sampling durations in the HFSD regime for animals in the 50-kg weight class but not in the 100-kg weight class. The evidence from these experiments indicates that the collection of multiple saliva samples does not affect salivary cortisol concentrations in grower pigs, but that collection of individual samples in as short a time as possible would be prudent to avoid sampling effects in younger animals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".