Persistent disturbance by commercial navigation alters the relative abundance of channel-dwelling fishes in a large river
Bibliographic record
Abstract
We provide the first evidence for chronic effects of disturbance by commercial vessels on the spatial distribution and abundance of fishes in the channels of a large river. Most of the world's large rivers are intensively managed to satisfy increasing demands for commercial shipping, but little research has been conducted to identify and alleviate any adverse consequences of commercial navigation. We used a combination of a gradient sampling design incorporating quasicontrol areas with Akaike's information criterion (AIC)-weighted model averaging to estimate effects of disturbances by commercial vessels on fishes in the upper Mississippi River. Species density, which mainly measured species evenness, decreased with increasing disturbance frequency. The most abundant species — gizzard shad (Dorosoma cepedianum) and freshwater drum (Aplodinotus grunniens) — and the less abundant shovelnose sturgeon (Scaphirhynchus platorhynchus) and flathead catfish (Pylodictis olivaris) were seemingly unaffected by traffic disturbance. In contrast, the relative abundance of the toothed herrings (Hiodon spp.), redhorses (Moxostoma spp.), buffaloes (Ictiobus spp.), channel catfish (Ictalurus punctatus), sauger (Sander canadensis), and white bass (Morone chrysops) decreased with increasing traffic in the navigation channel. We hypothesized that the combination of alteration of hydraulic features within navigation channels and rehabilitation of secondary channels might benefit channel-dependent species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".