Spawning lake sturgeon (<i>Acipenser fulvescens</i> Rafinesque, 1817) and their habitat characteristics in Rainy River, Ontario and Minnesota
Bibliographic record
Abstract
Lake Sturgeon (Acipenser fulvescens) spawning habitat in the Rainy River was studied in light of the ongoing review of the rules governing water levels upstream that affect discharge rates through the International Falls dam. The objectives of this study were to assess the current status of spawning Lake Sturgeon below the dam by: (i) evaluating weight–length relationships, condition, age and growth; (ii) identifying spawning locations and characterizing its physical attributes; and, (iii) evaluating the effects of water surface elevation on the availability of spawning habitat. Spawning was confirmed with use of egg mats, and targeted sampling of spawning individuals was completed using gillnets and electrofishing. Physical attributes of spawning locations, including temperature, depth and water velocity were collected at a range of water levels using an Acoustic Doppler Current Profiler and representative sediment sampling. Biological characteristics and timing of spawning events were consistent with those previously reported for Lake Sturgeon. However, a wider range of depths (0.3–10.5 m) and velocities (0.01–1.9 m3/s) than reported in most single site studies was found here, with the ranges encompassing what is reported in the literature as a whole. Lake Sturgeon were found to prefer coarse spawning substrates dominated by bedrock, boulder or cobble. The availability of preferred spawning habitat varied significantly with the changing water levels observed during the study and was eventually limited at higher water levels by river channel form. Because of the implications for dewatering of nest sites, keeping flows constant during spawning is critical to Rainy River Lake Sturgeon spawning success.
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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".