Linking Landscapes and Food Webs: Effects of Omnivorous Fish and Watersheds on Reservoir Ecosystems
Bibliographic record
Abstract
Ecologists increasingly recognize the need to understand how landscapes and food webs interact. Reservoir ecosystems are heavily subsidized by nutrients and detritus from surrounding watersheds, and often contain abundant populations of gizzard shad, an omnivorous fish that consumes plankton and detritus. Gizzard shad link terrestrial landscapes and pelagic reservoir food webs by consuming detritus, translocating nutrients from sediment detritus to the water column, and consuming zooplankton. The abundance of gizzard shad increases with watershed agriculturalization, most likely through a variety of mechanisms operating on larval and adult life stages. Gizzard shad have myriad effects on reservoirs, including impacts on nutrients, phytoplankton, zooplankton, and fish, and many of their effects vary with ecosystem productivity (i.e., watershed land use). Interactive feedbacks among watersheds, gizzard shad populations, and reservoir food webs operate to maintain dominance of gizzard shad in highly productive systems. Thus, effective stewardship of reservoir ecosystems must incorporate both watershed and food-web perspectives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".