Does the strength of cross-ecosystem trophic cascades vary with ecosystem size? A test using a natural microcosm
Bibliographic record
Abstract
Agradecimentos: The authors thank T. Lewinsohn, J.F. Farjalla, F.R. daSilva, A.A.M. MacDonald for critically reading the first version of this manuscript. The authors acknowledge twoanonymous referees for providing valuable suggestionsthat significantly improved the quality of the manuscript. Thanks are also due to D.A. de Omena, G.C.O. Piccoliand A. Nishi for ?eld assistance, G.C.O. Piccoli and T. N.Bernabé for helping in insect identication, P.P.A. Antiqueira for statistical support on piecewise SEM analysis, the staff of the Parque Estadual da Ilha do Cardosofor the logistic support. P.M. de Omena received fellow-ship from São Paulo Research Foundation (FAPESP:2009/51702-0), 'Coordenação de Aperfeiçoamento de Pessoal de Nível Superior’ and Department of Foreign Affairs, Trade and Development Canada (CAPES-DFAIT:BEX 8377/12-0). G. Q. Romero was supported by the ‘Conselho Nacional de Desenvolvimento Cientíco e Tecnológico’ (CNPq) research grant, and D.S. Srivastava byNatural Science and Engineering Research Council(Canada) Discovery and Accelerator Grants. This studywas funded by FAPESP
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".