Data from: Collective aggressiveness of an ecosystem engineer is associated with coral recovery
Bibliographic record
Abstract
The ecological impacts of animal groups may be different and predictable depending on their collective behavior. Farmerfish (Stegastes nigricans) live in social groups and collectively defend gardens of palatable algae. These gardens also serve as settlement and nursery habitats for corals because farmerfish mob corallivores that attempt to forage on corals within their gardens. We detected large among-colony differences in farmerfish collective aggression towards intruder fish that persist across years. We further found that the territories of aggressive groups and territories containing larger farmerfish provided greater protection to corals than non-aggressive groups: territories of aggressive groups naturally harbored more branching corals than non-aggressive groups, and experimentally outplanted branching corals experienced 80% less skeletal loss and grew larger over 25 weeks in aggressive territories and in territories guarded by larger fish. These findings hint that factors that increase farmerfish group aggressiveness (e.g., higher temperatures) could enhance the protective value of their territories for the replenishment of coral populations.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.023 |
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".