Gradient Boosted Trees can deliver more than accurate ecological predictions
Bibliographic record
Abstract
Response to Recommender Additional clarifications in the methodology are requiredBoth reviewers are positive about the study, and their comments generally deal with (i) a better justification of methodological choices, and (ii) more transparency in the interpretation of the results, in particular when it comes to discussing causal relationships inferred with the GBM.-We thank you for facilitating the review process.Responding to the comments resulted in a much stronger manuscript.(i) We have included additional justification for using gradient boosting rather than other machine learning methods and for why the results are unlikely to be contingent on our specific methodological choices during feature selection.(ii) We have also added a more nuanced discussion concerning the inference of ecological relationships using GBMs.These methods identify important hypotheses about correlations in the data but we recognize that, without experimentation, we cannot infer their ecological meaning.We now better emphasize that these relationships are examples supporting the point that GBMs are a powerful tool for automatically detecting complex relationships from large ecological datasets.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".