Age, Growth, Relative Abundance, and Scuba Capture of a New or Recovering Spawning Population of Lake Sturgeon in the Lower Niagara River, New York
Bibliographic record
Abstract
Abstract The objective of our study was to collect age, growth, and catch-per-unit-effort information from a new or recovering population of lake sturgeon Acipenser fulvescens in the lower Niagara River, New York. From July 1998 through August 2000, we captured 67 lake sturgeon by use of gill nets, baited setlines, and scuba diving. Active capture by scuba divers (1.50 fish/ h) was much more effective than passive capture with gill nets (0.07 fish/h) and setlines (0.06 fish/h). Eggs of Chinook salmon Oncorhynchus tshawytscha were more effective as setline bait than were alewives Alosa pseudoharengus, but neither bait differed in effectiveness from rainbow smelt Osmerus mordax. Ages of captured lake sturgeon ranged from 1 to 23 years; 47 of the 61 aged fish were younger than age 10. Strong relationships were found between weight, W, and length, L (W = 0.0000005 · L3.5564; R2 = 0.977) and between L and age (L = 394.05 · loge(age) + 248.77; R2 = 0.878). The lake sturgeon population in the lower Niagara River is probably small relative to its historic abundance. This naturally reproducing population should remain listed as threatened by New York State, and commercial and recreational fisheries should remain closed so that the population can rebuild adult numbers and reproductive potential.
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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 | 0.001 |
| 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".