A regression mixture with logistic weights for modeling heterogeneous crustacean growth data when the maturity phase is unknown
Bibliographic record
Abstract
Piecewise linear models (PLMs) have been used widely in crustacean morphometry and growth modeling when subjects belong to more than one undetermined maturity stage, for example, to model immature and adolescent male snow crab (Chionoecetes opilio) growth increment at moulting as a function of pre-moult size. PLMs assume, unrealistically, that the transition between the maturity stages occurs at a fixed size. PLMs have also often been applied without taking into account the heterogeneity of variance or without supplying parameter confidence intervals. We propose to describe the unknown maturity stage and the known growth increments as functions of the pre-moult size by regression mixture with logistic weights (RMLW), where a logistic function and two linear regressions are mixed with weights provided by the logistic function. Errors are assumed to be normally distributed with a standard deviation depending linearly on the expected growth increment. Parameters and their confidence intervals are obtained using maximum likelihood. Model diagnostic procedures and a posteriori maturity stage classification methods are presented. The methodology is applied to 1311 snow crab growth observations, and results are compared with those of other snow crab studies. The methodology has widespread applications to crustacean morphometry and can be generalized to nonlinear relationships.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".