177 Quantifying Cortisol in Hair as a Chronic Stress Biomarker in Group-housed and Stall-housed Sows During Gestation
Bibliographic record
Abstract
Abstract The objective of the study was to determine hair cortisol concentrations in sows in two different housing systems. Sows (n = 66, parity 0–6) were housed at the SDSU sow facility and assigned to one of two housing systems, stalls (STL) or group pens (PEN). The STL sows (n = 34) were housed in gestation stalls from breeding until d111 ± 1.1 of gestation; PEN sows (n = 32) were moved to 3 pens approximately 24 h after breeding in a dynamic group sow housing system. All females were housed in stalls at least 5d prior to breeding. All sows were moved to farrowing crates at approximately d111 of gestation. Hair was shaved from the right hip within 5d of breeding (defined as d0). At d37, d74, and d111 of gestation the same area was shaved and hair collected; samples from d37 and d111 were analyzed for cortisol. In the statistical model, main effects of housing system, time, and their interactions were tested with parity as random effect. Sows were later assigned a parity group [0–1 (n = 23), 2–3 (n = 17), and 4–6 (n = 26)] to assess the interactions between parity and treatment. There was a treatment by parity interaction (P < 0.05) where parity 0–1 STL group had higher cortisol (75.6 pg/mg) than parity 0–1 PEN group (24.2 pg/mg) and no effect of housing on parity 2–3 and 4–6 groups. Across parity, STL sows had greater (P < 0.05) overall hair cortisol than PEN sows (49.4 vs 19.8 ± 8.0 pg/mg hair). Hair cortisol concentration tended to be lower (P = 0.06) at d37 than d111 (29.4 vs 39.8 ± 8.0 pg/mg) and no time by treatment interaction was observed. These results suggest that young sows experience greater stress in individual stall housing than in group housing and that cortisol increases with progressing gestation regardless of housing system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".