Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science
Bibliographic record
Abstract
There is a growing realization among scientists and policy makers that an increased understanding of today's environmental issues requires international collaboration and data synthesis. Meta‐analyses have served this role in ecology for more than a decade, but the different experimental methodologies researchers use can limit the strength of the meta‐analytic approach. Considering the global nature of many environmental issues, a new collaborative approach, which we call coordinated distributed experiments (CDEs), is needed that will control for both spatial and temporal scale, and that encompasses large geographic ranges. Ecological CDEs, involving standardized, controlled protocols, have the potential to advance our understanding of general principles in ecology and environmental science.
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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 | 0.053 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".