The Behavior of Animals: Mechanisms, Function, and Evolution
Bibliographic record
Abstract
Foreword: Robert A. Hinde, University of Cambridge.1. The Study of Animal Behavior: Johan J. Bolhuis and Luc-Alain Giraldeau.Part I: Mechanisms of Behavior.2. Stimulus Perception: Jorg-Peter Ewert, Universitat Kassel.3. Motivation: Jerry A. Hogan, University of Toronto.4. Biological Rhythms and Behavior: Ralph E. Mistlberger, Simon Fraser University, and Benjamin Rusak, QEII Health Sciences Centre.5. Brain and Behavior: David F. Sherry, University of Western Ontario.6. Development of Behavior: Johan J. Bolhuis.7. Learning and Memory: Kimberly Kirkpatrick, University of York, and Geoffrey Hall, University of York.8. Animal Cognition: Nathan Emery, University of Cambridge, and Nicola S. Clayton, University of Cambridge.Part II: Function and Evolution of Behavior.9. The Function of Behavior: Luc-Alain Giraldeau.10. Communication: Peter K. McGregor, University of Copenhagen.11. Mate Choice, Mating Systems and Sexual Selection: Anders Pape Moller, Universite Pierre et Marie Curie.12. Sperm Competition and Sexual Conflict: Mark A. Elgar, University of Melbourne.13. Evolution of Behavior: Michael J. Ryan, University of Texas.14. Social Systems: Anne Pusey, University of Minnesota.Part III: Animal Behavior and Human Society.15. Applied Animal Behavior and Animal Welfare: David Fraser and Daniel Weary, both University of British Columbia.16. Animal Behavior and Conservation Biology: Tim Caro, University of California at Davis, and John Eadie, University of California at Davis.17. Human Behavior as Animal Behavior: Martin Daly, McMaster University, and Margo Wilson, McMaster University.Glossary.References.Author Index.Subject Index
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.025 |
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".