Behavioral Ecology of Captive Species: Using Behavioral Adaptations to Assess and Enhance Welfare of Nonhuman Zoo Animals
Bibliographic record
Abstract
This project aimed to estimate a species' adaptations in nature and in captivity, assess welfare, suggest environmental changes, and find species characteristics that underlie welfare problems in nonhuman animals in the zoo. First, the current status of zoo animal welfare assessment was reviewed, and the behavioral ecology approach was outlined. In this approach, databases of species characteristics were developed using (a) literature of natural behavior and (b) captive behavior. Species characteristics were grouped in 8 functional behavioral ecological fitness-related categories: space, time, metabolic, safety, reproductive, comfort, social, and information adaptations. Assessments of the strength of behavioral adaptations in relation to environmental demands were made based on the results available from the literature. The databases with literature at the species level were coupled with databases of (c) behavioral observations and (d) welfare assessments under captive conditions. Observation and welfare assessment methods were adapted from the animal on the farm realm and applied to zoo species. It was expected that the comparison of the repertoire of behaviors in natural and captive environments would highlight welfare problems, provide solutions to welfare problems by environmental changes, and identify species characteristics underlying zoo animal welfare problems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".