Validating owner-reporting of feather condition of pet Psittaciformes using photographs
Bibliographic record
Abstract
Abstract Reporting of outcome variables by caregivers in welfare studies is commonplace but is open to subjective bias and so requires validation. Biases can occur in either direction: familiarity with an animal allows a deeper insight into welfare problems, but also can lead to reticence in admitting that an animal in one's care is experiencing problems. Here, we aim to validate owner-reporting of plumage condition of pet parrots, including those with self-inflicted feather-damaging behaviour (FDB), by comparing owners’ scores of feather condition with those of two independent raters, blind to the owners’ and each other's assessments. We surveyed pet parrot owners to collect data on basic demographics and feather condition, and requested four standardised photographs of birds. We received 259 responses (17% of the 1,521 people contacted); 78 sets of images of appropriate quality for assessment by raters were provided. Mean percentage agreement between owners’ and raters’ scores was mostly fair to substantial using Cohen's kappa; however, raters scored a greater proportion of feather damage than did owners. Overall, our results indicate owner-reporting of feather condition, including FDB, to be generally reliable and consistent with independent assessment of photographs. As the use of photographs can be limited by image quality, a failure to represent the long-term state of a parrot, and the potential for incorrect recording if assessed without relevant information (eg on moulting), this evidence that owner-reports can be reliable opens the door for larger-scale surveys of the extent of welfare-relevant 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 | 0.012 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".