Bibliographic record
Abstract
The selection of ambassador animals coming out of wildlife rehabilitation facilities is an evolving process as information grows regarding long–term physical impacts of disabilities on an animal’s quality of life. Ambassador animal welfare traditionally addressed nourishment, length of life, and physical safety while in human care. More facilities are now focusing on cognitive well–being, including examining if individuals are free from pain, fear, and distress as a measure of welfare. And, as more trainers are adopting choice–based training methods using the least number of aversive stimuli possible, candidate selection is the first step in the welfare process. Cascades Raptor Center has developed rigorous criteria for all birds before they are added to our team. Because many of our resident birds are wild–hatched individuals deemed non–releasable by rehabilitation facilities, it became necessary to devise a thorough assessment process. Data collected from wellness monitoring of our current bird collection coupled with over 25 years of comprehensive necropsy reports have provided information indicating that many disabilities that result in non–releasable status also preclude individuals from having a high quality of life in human care. Setting an ambassador animal up for a successful life in human care begins with appropriate, well considered selection.
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.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".