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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".