Bibliographic record
Abstract
Abstract Conservation biology and animal welfare science are multidisciplinary fields of research that address social concerns about animals. Conservation biology focuses on wild animals, works at the level of populations, ecological systems and genetic types, and deals with threats to biodiversity and ecological integrity. Animal welfare science typically focuses on captive (often domestic) animals, works at the level of individuals and groups, and deals with threats to the animals’ health and quality of life. However, there are many areas of existing or potential overlap: (i) many real-life problems, such as environmental contamination, urban development and transportation, create problems for animals that involve both welfare and conservation; (ii) research methods from each field are needed to address some of the scientific problems of the other; and (iii) policies and practices targeting either conservation or animal welfare may prove unproductive if they do not take account of both areas of concern. Moreover, scientists in both fields face the common challenge of applying science to guide policy and practice, often to issues that are both empirical and ethical, and often under conditions of uncertainty. There are many cases where communication and co-operation between the fields should lead to better science and better practical outcomes.
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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".