Picture Perfect Pups: How Do Attributes of Photographs of Dogs in Online Rescue Profiles Affect Adoption Speed?
Bibliographic record
Abstract
To increase the public's awareness of and exposure to animals needing homes, PetRescue, Australia's largest online directory of animals in need of adoption, lists all currently available animals from rescue and welfare shelters nationwide. The current study examined the photographs in the PetRescue online profiles of the three most common breeds within these data, namely, Staffordshire bull terriers (n = 3988), Labrador retrievers (n = 2246), and Jack Russell terriers (n = 2088), to identify the inferred preferences of potential adopters. By investigating the attributes of these photographs, we were able to identify visual risk factors associated with protracted lengths of stay (LOS). The longest stays were associated with dogs with erect ears and those photographed in a natural environment, i.e., 18.32 days and 19.57 days, respectively. Dogs photographed in a kennel and with mouths closed had the shortest LOS, i.e., 11.54 d and 14.44 d, respectively. Heightened awareness of the roles of photographic attributes in generating interest among potential adopters may increase the speed of adoption by guiding the creation of online profiles and selection of photos to optimise the promotion of dogs at risk of long stays.
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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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".