The automated analysis of clustering behaviour of piglets from thermal images in response to immune challenge by vaccination
Bibliographic record
Abstract
An automated method of estimating the spatial distribution of piglets within a pen was used to assess huddling behaviour under normal conditions and during a febrile response to vaccination. The automated method was compared with a manual assessment of clustering activity. Huddling behaviour was partly related to environmental conditions and clock time such that more huddling occurred during the night and at lower ambient air temperatures. There were no positive relationships between maximum pig temperatures and environmental conditions, suggesting that the narrow range of air temperatures in this study was not a significant factor for pig temperature. Spatial distribution affected radiated pig temperature measurements by IR thermography. Higher temperatures were recorded in groups of animals displaying huddling behaviour. Huddling behaviour was affected by febrile responses to vaccination with increased huddling occurring 3 to 8 h post-vaccination. The automated method of assessing spatial distribution from an IR image successfully identified periods of huddling associated with a febrile response, and to changing environmental temperatures. Infrared imaging could be used to quantify temperature and behaviour from the same images.
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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 | 0.001 |
| 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".