Bibliographic record
Abstract
Nonhuman animal welfare is of significant public interest, globally and within the United States. Value-based judgments are intrinsic to animal welfare assessment, according to the relative weighting of factors associated with animal performance, health, affective states, and natural living. The concept of animal welfare is consistent with the scientific method because questions are open to deductive reasoning, formation of hypotheses and predictions, and collection and analysis of empirical data. Multidisciplinary techniques used in the laboratory are helpful to understanding a whole animal response to particular situations and are especially important in interpretation of data about affective states. Epidemiological techniques can be used to identify prevalence and risk factors associated with particular animal welfare challenges in field conditions and are particularly useful for motivating change and evaluating the effectiveness of interventions intended to improve animal welfare on farms. Compromised animals who are affected by injury or illness represent a vulnerable population with unique animal welfare challenges for which laboratory and field-based studies are needed.
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 | 0.116 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.086 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".