Variation in lake sturgeon (<i>Acipenser fulvescens</i>) abundance and growth among river reaches in a large regulated river
Bibliographic record
Abstract
Lake sturgeon (Acipenser fulvescens) stocks are well below historical levels across their natural range. In this study, we examine why lake sturgeon have not substantially recovered to historical levels in a large regulated river (Ottawa River, Canada). Three primary anthropogenic stressors have been identified as potentially limiting lake sturgeon populations in the Ottawa River: (i) commercial harvest, (ii) contaminants, and (iii) water power management. Hypotheses i and iii were tested by comparing lake sturgeon abundance and examining growth among reaches differing in level of commercial harvest and water management regime; hypothesis ii was tested by assessing contaminant loads in lake sturgeon and examining effects on growth and condition. Relative abundance, growth, mortality, and mean size of lake sturgeon did not differ among river reaches with (n = 6) and without (n = 3) a commercial harvest. Mercury was the only contaminant that was elevated. Neither growth nor condition showed any detectable relationship with mercury body burden. Relative abundance of lake sturgeon was greater in unimpounded than impounded reaches; additionally, there is evidence of faster growth in the impounded versus unimpounded reaches, suggesting density-dependent compensation. Water power management appears to be the primary factor affecting lake sturgeon in this 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".