lichengxue/MSE_NAA_Project: CJFAS-MSE-NAA
Bibliographic record
Abstract
Random effects on numbers-at-age transitions implicitly account for movement dynamics and improve stock assessment and management This repository contains all materials (manuscript, code, and archived packages) for the paper: Li, C., Deroba, J. J., Berger, A. M., Goethel, D. R., Langseth, B. J., Schueller, A. M., & Miller, T. J. (2025). Random effects on numbers-at-age transitions implicitly account for movement dynamics and improve stock assessment and management. Canadian Journal of Fisheries and Aquatic Sciences. Release v1.1 This release corresponds to the final accepted version of the manuscript. It includes: Manuscript source and compiled PDF All figures, tables, and supplementary materials Full R/TMB code to reproduce analyses Archived WHAM and whamMSE package versions used in simulations Response-to-reviewers letter and revision documents Repository structure Code-CJFAS/ — Main R and TMB scripts used to run simulations, estimation models, and generate manuscript figures Final/ — Final accepted manuscript (PDF / Rmd) and associated figures and tables Revision/ — Revised manuscript files and response-to-reviewers materials wham-CJFAS/wham/ — Archived WHAM package version used for simulations (backup only) whamMSE-CJFAS/whamMSE/ — Archived whamMSE package version used for simulations (backup only) README.md — Repository overview and instructions LICENSE — License for code use Getting started Clone the repository: git clone https://github.com/lichengxue/MSE_NAA_Project.git cd MSE_NAA_Project
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.573 | 0.480 |
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".