Disentangling food-web environment relationships: A review with guidelines
Bibliographic record
Abstract
Food webs represent the energy fluxes and the nutrient cycling between interacting species that underpin several ecosystem functions. Whether and how interactions vary across environmental gradients is still largely unknown. We reviewed the literature searching for systematic relationships between structural food-web properties and environmental gradients. Temperature and biotic factors are amongst the most frequently addressed drivers of food-web structure. We also assessed the degree to which food-web ecology has accomplished a mechanistic understanding of ecosystem functioning. We found that most studies are one-off descriptions of local food webs making it difficult to achieve an understanding of the response to human or environmental gradients. The lack of a consistent theory predicting how food webs change across environmental gradients, the diversity of objectives in food-web studies, and the absence of a standardized methodology for analysing them severely limit progress in the field. Moving forward requires the establishment of a core set of testable predictions, agreed standards for data collection and analysis, and the development of geographically distributed experimental studies of food-web dynamics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".