Bibliographic record
Abstract
With its roots firmly planted in behaviorist and animal learning traditions, lab-based research is an enduring and pervasive characteristic of comparative cognition. In this review, we discuss progress in comparative cognition research in other experimental settings such as zoos, captive animal parks, and wild settings. Zoos provide access to a large array of species housed in seminatural environments that allow a reasonable degree of experimental control. Thanks to the advent of computer technology, a wide range of complex cognitive processes is increasingly being successfully studied in zoo environments. Further, cognitive research provides enrichment for captive animal participants, reducing anxiety and promoting psychological well-being. The results of cognitive research also benefit the welfare of captive animals through preference assessment, species-specific exhibit design, and behavioral management. Field settings also offer unique advantages and have allowed researchers to systematically study such diverse topics as spatial cognition, cultural transmission, problem solving, and preference. Not only does field research expand our understanding of the evolutionary and ecological drivers of animal cognition, but it also can directly inform conservation efforts. Although venturing out of the lab presents tangible challenges, including the restriction of testable hypotheses and conclusions that can be inferred from results, the benefits to be gained outweigh the costs.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".