Speed of Dog Adoption: Impact of Online Photo Traits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet has radically changed how dogs are advertised for adoption in the United States. This study was used to investigate how different characteristics in dogs' photos presented online affected the speed of their adoptions, as a proof of concept to encourage more research in this field. The study analyzed the 1st images of 468 adopted young and adult black dogs identified as Labrador Retriever mixed breeds across the United States. A subjective global measure of photo quality had the largest impact on time to adoption. Other photo traits that positively impacted adoption speed included direct canine eye contact with the camera, the dog standing up, the photo being appropriately sized, an outdoor photo location, and a nonblurry image. Photos taken in a cage, dogs wearing a bandana, dogs having a visible tongue, and some other traits had no effect on how fast the dogs were adopted. Improving the quality of online photos of dogs presented for adoption may speed up and possibly increase the number of adoptions, thereby providing a cheap and easy way to help fight the homeless companion animal population problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it