A community genetics perspective: opportunities for the coming decade
Bibliographic record
Abstract
Summary Community genetics was originally proposed as a novel approach to identifying links between genes and ecosystems, and merging ecological and evolutional perspectives. The dozen years since the birth of community genetics have seen many empirical studies and common garden experiments, as well as the rise of eco‐evolutionary dynamics research and a general shift in ecology to incorporate intraspecific variation. So what have we learned from community genetics? Can individual genes affect entire ecosystems? Are there interesting questions left to be answered, or has community genetics run its course? This perspective makes a series of key points about the general patterns that have emerged and calls attention to gaps in our understanding to be addressed in the coming years. Contents Summary 65 I. Introduction 65 II. Plant genotype effects are common, especially on herbivores 66 III. From prey to predators, genetic effects cascade up the food chain 66 IV. Genetic effects rain down from the canopy to soil 66 V. The dampening of the extended phenotype 66 VI. From genetic to species, diversity begets diversity 67 VII. It's the traits that matter … but which ones? 68 VIII. There are many, many genes for ecosystems 68 IX. From the community‐genetic caterpillar to the eco‐evolutionary butterfly 68 X. Conclusions and future directions 68 Acknowledgements 69 References 69
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".