Use of a food neophobia test to characterize personality traits of dairy calves
Bibliographic record
Abstract
Food neophobia, i.e. the avoidance of novel foods, is common in ruminants and may provide a biologically relevant and practical way to test individual responses to novelty or challenge. We aimed to determine if behavioural responses in a food neophobia test (exposure to a novel total mixed ration) reflected boldness and exploratory personality traits derived from 3 traditional tests (open field, novel human and novel object) in dairy calves. We performed two Principal Component Analyses, one using behaviours from 3 traditional tests (3 factors: 'Bold', 'Exploratory' and 'Active'), and one using behaviours from the food neophobia test (3 factors: 'Eating', 'Inspecting', and 'Avoidance'). A regression analysis determined if individual factor scores from the food neophobia test predicted factor scores from the traditional tests. Contrary to our expectations, 'Avoidance' (latencies to approach and eat the novel food) did not predict boldness trait, and the factors 'Inspecting' (time spent inspecting food and empty buckets) and 'Eating' (time spent eating food and total intake) did not predict exploration trait, but they did predict active trait. These results suggest that the food neophobia test in our study resulted in context-specific behaviours, or that behavioural responses to a novel food present different underlying personality traits. The application of food neophobia to assess specific or generalized personality traits of dairy calves deserves further work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".