Length Frequency Age Estimations of American Eel Recruiting to the Upper St. Lawrence River and Lake Ontario
Bibliographic record
Abstract
Abstract We applied length frequency analysis (LFA) with multiple model inference to estimate the age structure of American EelAnguilla rostratarecruiting to the upper St. Lawrence River and Lake Ontario system between 1975 and 2008. The LFA results were used to derive a composite relative abundance index for 48 cohorts from 1967 to 2004. The size of eel recruits was influenced by both year and season and consistently increased from July to October. Age composition and mean length at age varied greatly from year to year. On average, eels spent about 6 years in the lower St. Lawrence River before they recruited to Lake Ontario. A nonsymmetric distribution function, such as lognormal or Gamma function, was selected to describe length‐at‐age observations of American Eels. Recruiting cohorts appeared to be relatively strong from the late 1960s to the late 1970s but subsequently declined exponentially. Some recovery signs were found for the last 8 years, but cohorts have been weak since 1988. A validation study showed that the LFA with multiple model inference approach can successfully estimate the age structure from length frequency observations. Our results provide scientific support for underlying eel habitat restoration and protection efforts in Lake Ontario and the upper St Lawrence 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".