Integrating the bright and dark sides of aquatic resource subsidies -- a synthesis
Bibliographic record
Abstract
Aquatic and terrestrial ecosystems are linked through the reciprocal exchange of materials and organisms. Aquatic-to-terrestrial subsidies are relatively low quantity in most terrestrial ecosystems, but they can provide high contents of limiting resources that increase consumer fitness and ecosystem production. However, they also may carry significant contaminant loads, particularly in anthropogenically-impacted watersheds. Global change processes, including land use change, climate change, and biodiversity declines, are altering the quantity and quality of aquatic subsidies, potentially shifting the balance of costs and benefits of aquatic subsidies for terrestrial consumers. Many global change processes interact and impact both the bright and dark sides of aquatic subsidies simultaneously, highlighting the need for future integrative research that bridges ecosystem as well as disciplinary boundaries. We identify key research priorities, including increased quantification of the spatiotemporal variability in aquatic subsidies across a range of ecosystems, greater understanding of the landscape-scale extent of aquatic subsidy impact, and deeper exploration of the relative costs and benefits of aquatic subsidies for consumers.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".