Beyond “Doing Better”: Improving the Objectivity of Cat Behavior Assessment
Bibliographic record
Abstract
At Toronto Humane Society, inconsistencies in how cat behavior and welfare were being reported led to the development of a new system using four simple, standardized behavior rating scales. These ordinal rating scales — measuring fear, anxiety, and stress; response to petting; participation in play; and food intake — use a 0 to 5 scale to track subtle changes over time. This approach allows staff and volunteers to report behavior more clearly and consistently, making it easier to monitor progress and make informed decisions about care, interventions, and placement. The scales were designed to be easy to use in a busy shelter environment while still providing meaningful data. A key part of the system’s success is training — ensuring that different people interpret behaviors the same way. The author has recently released a free online version of the training offering CEU credits from IAABC and CCPDT, now available to anyone interested in applying the scales in shelters, clinics, or homes. Since implementing this system, Toronto Humane Society has seen more efficient case management and increased feline welfare, ultimately improving adoptions. The scales have helped guide adjustments to behavior plans, evaluate the efficacy psychopharmaceuticals, and support decisions for alternative placements. By offering a reliable way to monitor feline welfare, this tool can help shelters and professionals everywhere better understand and support the cats in their care.
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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 | 0.184 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".