Using culturally transmitted behavior to help delineate conservation units for species at risk
Bibliographic record
Abstract
Culture (information or behavior acquired by social learning and shared by members of a community) is an inheritance system that that can contribute to the designation of conservation units for species at risk. The phenotypic diversity produced by culture is of intrinsic value and behaviorally-cohesive communities or sets of communities may be suitable candidate conservation units. This paper considers how cultural information can contribute to the designation of conservation units, in particular when assessing the discreteness and/or evolutionarily significance of potential units. Call and song dialects are particularly useful for documenting discreteness, while differences in seasonal migrations, if consistent, can be evolutionarily significant. Distinctions in foraging behavior or diet can suggest discreteness and/or evolutionary significance but it is important to show these are not environmentally driven. Social and play behavior can also be used to show discreteness. In some cases, it may not be clear whether behavioral differences are genetically or culturally determined but this may not matter for the delineation of conservation units if the behavioral distinctions are heritable. Genetic correlations can indicate the stability of culturally-determined behavior when transmission processes are parallel (e.g., mitochondrial DNA and behavior learned from the mother). The explicit use of cultural data in the delineation of conservation units is currently rare, but should increase as more detailed and extensive behavioral databases are compiled and analytical methods are developed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".