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 small 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 impacts and deeper exploration of the relative costs and benefits of aquatic subsidies for consumers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".