Effects of reservoir connectivity on stream fish assemblages in the Great Plains
Bibliographic record
Abstract
The upstream effects of reservoirs on stream fish assemblages were highly localized in 3rd- through 5th-order streams in the Great Plains, USA. Streams that differed in connectivity to reservoirs were sampled at their confluences with a river or reservoir and between the confluence and the stream's origin. Sites at confluences had higher total, nonnative, and reservoir species richness than middle sites. Variability in fish assemblage structure upstream of reservoirs was influenced by catchment area, stream size, gradient, and reservoir connectivity. Confluence sites connected to reservoirs were correctly classified based on the presence of red shiners (Cyprinella lutrensis) and bluntnose minnows (Pimephales notatus) and the absence of sand shiners (Notropis stramineus); middle sites on connected streams were classified by the absence of redfin shiners (Lythrurus umbratilis). Intensive sampling across pool habitats within two streams isolated by a reservoir indicated that abundance of common reservoir species was related to pool size, turbidity, and canopy cover, but not proximity to the reservoir. These data suggest that streams connected to reservoirs can maintain diverse native fish communities with minimal invasions by reservoir-dwelling species, but a fraction of the community either has been lost or occurs at low abundance (e.g., sand shiners and redfin shiners).
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".