Predicting and prioritizing coexistence: learning outcomes via experiments
Bibliographic record
Abstract
Community assembly provides the foundation for applications in biodiversity conservation, climate change, invasion ecology, restoration ecology, and synthetic ecology. Predicting and prioritizing community assembly outcomes remains challenging. We address this challenge via a mechanism-free LOVE (Learning Outcomes Via Experiments) approach suitable for cases where little data or knowledge exist: we carry out actions (randomly-sampled combinations of species additions), measure abundance outcomes, and then train a model to predict arbitrary outcomes of actions, or prioritize actions that would yield the most desirable outcomes. When trained on <100 randomly-selected actions, LOVE predicts outcomes with 2-5% error across datasets, and prioritizes actions for maximizing richness, maximizing abundance, or minimizing abundances of unwanted species, with 94-99% true positive rate and 12-83% true negative rate across tasks. LOVE complements existing approaches for community ecology by providing a foundation for additional mechanism-first study, and may help address numerous ecological applications.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".