Juvenile green sturgeon (<i>Acipenser medirostris</i>) and white sturgeon (<i>Acipenser transmontanus</i>) behavior near water-diversion fish screens: experiments in a laboratory swimming flume
Bibliographic record
Abstract
Water diversions that extract fresh water for urban, industrial, and agricultural uses, as well as export to southern California, are prevalent throughout the Sacramento–San Joaquin watershed. Many water diversions are fitted with fish-exclusion screens designed to prevent fish from entrainment (i.e., being drawn in). The impact of fish screens on the behavior of migrating juvenile fishes remains largely unknown, especially for threatened species such as sturgeon. We placed individual juvenile green (Acipenser medirostris) or white (Acipenser transmontanus) sturgeon in a laboratory swimming flume in the presence of standard fish screens (2 mm bar spacing) at two field-relevant water velocities (20.4 ± 0.1 and 37.3 ± 0.3 cm·s−1). Fish were tested at 18 °C for 15 min during the day or night and in the presence of possible behavioral deterrents. Behavioral responses, including screen contacts, impingements, and time spent near screens were quantified. Green sturgeon contacted and impinged upon the screens twice as frequently as white sturgeon and also differed in how their behaviors were altered by water velocities and time of day. Our results are informative in developing effective management strategies to mitigate the impacts of water diversions on sturgeon populations and suggest that effective restoration strategies for both species should be considered separately.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".