Will a large complex system be productive?
Bibliographic record
Abstract
While the relationship between food web complexity and stability has been well documented, how complexity affects productivity remains elusive. In this study, we combine food web theory and a data set of 149 aquatic food webs to investigate the effect of complexity (i.e. species richness, connectance, and average interaction strength) on ecosystem productivity. We find that more complex ecosystems tend to be more productive, although different facets of complexity have contrasting effects. A higher species richness and/or average interaction strength increases productivity, whereas a higher connectance often decreases it. These patterns hold not only between realized complexity and productivity, but also characterize responses of productivity to simulated declines of complexity. Our model also predicts a negative association between productivity and stability along gradients of complexity. Empirical analyses support our predictions on positive complexity-productivity relationships and negative productivity-stability relationships. Our study provides a step forward towards reconciling ecosystem complexity, productivity and stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".