Fine-scale habitat selection of green sturgeon (<i>Acipenser medirostris</i>) within three spawning locations in the Sacramento River, California
Bibliographic record
Abstract
Vast sections of the Sacramento River have been listed as critical habitat by the National Marine Fisheries Service for green sturgeon spawning (Acipenser medirostris), yet spawning is known to occur at only a few specific locations. This study reveals the range of physical habitat variables selected by adult green sturgeon during their spawning period. We integrated fine-scale fish positions, physical habitat characteristics, discharge, bathymetry, and simulated velocity and depth using a two-dimensional hydraulic model (FaSTMECH). The objective was to create habitat suitability curves for depth, velocity, and substrate type within three known spawning locations over two years. An overall cumulative habitat suitability score was calculated that averaged the depth, velocity, and substrate scores over all fish, sites, and years. A weighted usable area index was calculated throughout the sampling periods for each of the three sites. Cumulative results indicate that the microhabitat characteristics most preferred by green sturgeon in these three spawning locations were velocities between 1.0 and 1.1 m/s, depths of 8–9 m, and gravel and sand substrate. This study provides guidance for those who may in the future want to increase spawning habitat for green sturgeon within the Sacramento River.
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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) | 0.000 | 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) | 0.000 | 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 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".