Evaluating life‐history strategies of reef corals from species traits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Classifying the biological traits of organisms can test conceptual frameworks of life-history strategies and allow for predictions of how different species may respond to environmental disturbances. We apply a trait-based classification approach to a complex and threatened group of species, scleractinian corals. Using hierarchical clustering and random forests analyses, we identify up to four life-history strategies that appear globally consistent across 143 species of reef corals: competitive, weedy, stress-tolerant and generalist taxa, which are primarily separated by colony morphology, growth rate and reproductive mode. Documented shifts towards stress-tolerant, generalist and weedy species in coral reef communities are consistent with the expected responses of these life-history strategies. Our quantitative trait-based approach to classifying life-history strategies is objective, applicable to any taxa and a powerful tool that can be used to evaluate theories of community ecology and predict the impact of environmental and anthropogenic stressors on species assemblages.
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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) | 0.006 | 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 it