Including unsexed individuals in sex-specific growth models
Bibliographic record
Abstract
Sexually dimorphic growth models are typically estimated by fitting growth curves to individuals of known sex. Yet, macroscopically ascribing sex can be difficult, particularly for immature animals. As a result, sex-specific growth curves are often fit to known-sex individuals only, omitting unclassified immature individuals occupying an important region of the age–length space. We propose an alternative whereby the sex of the unclassified individuals is treated as a missing data problem to be estimated simultaneously with the sex-specific growth models. The mixture model that we develop includes the biological processes of growth and sexual dimorphism. Simulations show that where the assumed growth model holds, the method improves precision and bias of all parameters relative to the data omission case. Ability to chose the correct combination of sex-specific and sex-generic parameters is also improved. Application of the method to two shark species, where sex can be ascribed from birth, indicates improvements in the fit but also highlights the importance of the assumed model forms. The proposed method avoids discarding unclassified observations, thus improving our understanding of dimorphic growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".