Data from: Ecosystem context illuminates conflicting roles of plant diversity in carbon storage
Bibliographic record
Abstract
Plant diversity can increase biomass production in plot‐scale studies, but applying these results to ecosystem carbon (C) storage at larger spatial and temporal scales remains problematic. Other ecosystem controls interact with diversity and plant production, and may influence soil pools differently from plant pools. We integrated diversity with the state‐factor framework, which identifies key controls, or ‘state factors’, over ecosystem properties and services such as C storage. We used this framework to assess the effects of diversity, plant traits and state factors (climate, topography, time) on live tree, standing dead, organic horizon and total C in Québec forests. Four patterns emerged: (1) while state factors were usually the most important model predictors, models with both state and biotic factors (mean plant traits and diversity) better predicted C pools; (2) mean plant traits were better predictors than diversity; (3) diversity increased live tree C but reduced organic horizon C; (4) different C pools responded to different traits and diversity metrics. These results suggest that, where ecosystem properties result from multiple processes, no simple relationship may exist with any one organismal factor. Integrating biodiversity into ecosystem ecology and assessing both traits and diversity improves our mechanistic understanding of biotic effects on ecosystems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".