Monte Carlo evaluation of a nonlinear regression estimator for aggregated lengthweight data
Bibliographic record
Abstract
Parameters of fish lengthweight relationships (W = aLb) are usually estimated by applying linear regression to log-transformed length and weight values, but measuring individual weights is time-consuming and expensive. Often, length and weight data are available as sets of length measurements and aggregated sample weights, and the aggregate average weight of a sample can be expressed as the average of the weights predicted for the individual fish lengths. This study evaluated the feasibility of applying nonlinear regression to aggregated lengthweight data. Experiments with simulated random lengthweight data demonstrated that the estimates of parameter b appear to be unbiased and the estimates of a are right-skewed and biased. Further, the estimates of ln(a) and b are almost perfectly correlated. The precision and accuracy of the estimates were greatly influenced by the number of aggregate samples but were relatively unaffected by the number of fish in each sample. An additional experiment showed that the residuals from the regression can be used to detect small changes in the lengthweight parameters.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".