The Effect of a Change in Perception of Length Distribution of a Population on Maturity-at-age,
Bibliographic record
Abstract
The calculation of proportion mature-at-age and mean weight-at-age from length stratified sampling are dependent on the length distribution of the population. The Dept. of Fisheries and Oceans in St. John's, Newfoundland changed its survey bottom trawl in the autumn of 1995. Comparative fishing experiments between the old and new fishing gears showed that the new gear (Campelen) caught more small fish of most species than the old (Engel) gear. Conversions of the Engel time series to Campelen equivalents results in an increase in the number of small fish in the population. This paper examines the effect of this change on maturity-at-age, mean weight-at-age and spawning stock biomass (SSB) for American plaice (Hippoglossoides platessoides). The shift in the perceived length frequency distribution of the population results in an increase in age at 50 % maturity (the fish appear to be maturing later) and a decrease in mean weight-at-age. Spawning stock biomass calculated from these parameters is higher for the Campelen equivalent data because of the increased abundance at age. Trends in maturity-at-age, mean weight-at-age and SSB over the time period are generally the same for the Engel and Campelen equivalent data. Constructing a time series, which consists of unconverted Engel data followed by Campelen data, can be misleading. The change in estimated maturity and weight-at-age, and SSB has implications for both biological studies and the setting of reference levels.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".