Collective decision‐making promotes fitness loss in a fusion‐fission society
Bibliographic record
Abstract
While collective decision-making is recognised as a significant contributor to fitness in social species, the opposite outcome is also logically possible. We show that collective movement decisions guided by individual bison sharing faulty information about habitat quality promoted the use of ecological traps. The frequent, but short-lived, associations of bison with different spatial knowledge led to a population-wide shift from avoidance to selection of agricultural patches over 9 years in and around Prince Albert National Park, Canada. Bison were more likely to travel to an agricultural patch for the first time by following conspecifics already familiar with agricultural patches. Annual adult mortality increased by 12% due to hunting of bison on agricultural lands. Maladaptive social behaviour accordingly was a major force that contributed to a ~50% population decline in less than a decade. In human-altered landscapes, social learning by group-living species can lead to fitness losses, particularly in fusion-fission societies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".