In-depth analysis of the emotional reactivity of American mink (<i>Neovison vison</i>) under behavioral tests
Bibliographic record
Abstract
Currently, the importance of research into the behavior of farm animals is being highlighted. Monitoring animal temperament is a key aspect in improving their well-being. The aim of the study was to search for indicators facilitating classification of the emotional reactivity of mink (Neovison vison) using behavioral tests: glove and empathic tests. Both tests were applied to 760 mink in triplicate to assess their behavioral profile. Based on the obtained assessments, the animals subjected to the empathic test were classified into one of the four behavioral profiles: aggressive, curious, fearful, and neutral. The contact time of the mink with the new object in the empathic test clearly differentiates all four types of mink behavior. The significantly longest contact with the object was found for aggressive, and the shortest for neutral animals. The intensity and time of contact with the object may be an indicator of the behavioral profile of mink. Based on the results, it can be concluded that an additional predictor of the mink reactivity during behavioral tests should be taken into account, the intensity of contact with the object measured both by the intensity of direct contact and by the duration of this contact. The results shown in this study can be used as a selection indicator by including the temperament of farm mink in selection.
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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".