Bibliographic record
Abstract
Animal models of psychiatric conditions such as anxiety and depression attempt to represent some aspect of the etiology, symptomatology, or treatment of these disorders, in order to facilitate their scientific study (Treit, 1994; Mineka, 1985; Marks, 1987; Willner, 1994). Within this broad context, animal models of anxiolytic or antidepressant drug action can be viewed as treatment models concerned with the pharmacological control of human anxiety and depression. Animal models of anxiety, for example, have been particularly useful in the preclmical testing of benzodiazepme-type anxiolytics, in studying the functional relevance of the GABA A -benzodiazepine receptor system, and in characterizing the effects of benzodiazepine antagonists (e.g., Ro 15–1788 [flumazeml]), partial agonists (e.g., CGS 98961, and inverse agonists (e.g., B-CCM). For reviews see Thlebot (1983); La1 and Emmett-Oglesby (1983); File (1984;1985;1987); Crawley (1985); Treit (1985a;1994); Shephard (1986); Gardner (1988); Thiebot et al. (1988); and Lister (1990). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".