Identifying and characterizing juvenile lake sturgeon ( <i>Acipenser fulvescens</i> , Rafinesque, 1817) occupancy hot spots within the St. Clair‐Detroit River System
Bibliographic record
Abstract
Over the past two decades, extensive monitoring has been conducted in the St. Clair – Detroit River System to describe spatial and temporal patterns of lake sturgeon (Acipenser fulvescens). To characterize spatial patterns in juvenile lake sturgeon (<1000 mm TL) based on survey collections, ‘hot spots’ were identified through optimized hot spot analysis (HSA). This HSA was then interpolated by inverse distance weighted analysis to determine extent of identified ‘hot spots’ and ‘cold spots’. Additionally, habitat variables (i.e., water depth, water velocity, and dominant substrate type) were investigated using a single season occupancy model to determine their influence on juvenile lake sturgeon occupancy probability. In total, 1203 juvenile lake sturgeon were captured across 4197 surveys. Three unique ‘hot spots’ were identified; western Lake Erie, Fighting Island in the Detroit River, and the North Channel in the St. Clair River. Interpolated ‘hot spots’ encompassed 73.1 km² in western Lake Erie, 4.7 km² near Fighting Island, and 6.6 km² in the North Channel. Detection probabilities within ‘hot spots’ ranged from 8.8%–43.4%. No habitat variables significantly predicted juvenile lake sturgeon occupancy. Juvenile lake sturgeon were captured in western Lake Erie where the water depth was >5.1 m and odds of occupancy increased with increased water velocity. Juvenile lake sturgeon in the Detroit and St. Clair River ‘hot spots’ were captured at sites with mean benthic water velocities ranging from 0.20–0.60 m/s and where water depth was >7.3 m. Irrespective of waterbody, 69% of all juveniles were detected over dominant sand and gravel substrates. These results provide valuable insight about juvenile habitat use that can help managers formulate effective conservation and restoration strategies supporting the continued recovery of Great Lakes lake sturgeon.
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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 | 0.001 | 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".