Development and evaluation of a habitat suitability model for young lake sturgeon (<i>Acipenser fulvescens</i>) in the North Channel of the St. Clair River, Michigan
Bibliographic record
Abstract
We evaluated the quantity, quality, and spatial distribution of riverine nursery habitat for larval, young-of-year (YOY), and juvenile (<500 mm) lake sturgeon (Acipenser fulvescens) in the North Channel of the St. Clair River (SCR), Michigan, using habitat suitability modeling (HSM) and fish collections. This HSM was developed using georeferenced habitat information on substrates, invertebrate densities, benthic flow velocities, and water depth. High-quality habitat comprised 29.1% of the study area, primarily in river bends and other areas where water velocities were highly varied. In all, 283 larval lake sturgeon were collected, with the majority found in high-quality habitat (81%) and heavily clustered in three locations (91.2% of total catch). Substrate composition and benthic flow velocity were the best indicators of observed larval catch-per-unit-effort (CPUE) patterns. Available data on captured YOY and juveniles were used to corroborate modeled habitat output and revealed strong associations between modeled habitat conditions and young lake sturgeon presence. An abundance of suitable habitat for young lake sturgeon within the North Channel of the SCR suggests that lack of suitable spawning habitat or high mortality early in life limits recruitment success in this system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".