Recent advances in statistical methodology applied to the Hjort liver index time series (1859–2012) and associated influential factors
Bibliographic record
Abstract
Certain recent advances in statistical methodology have promising potential for fruitful use in general biology and the fisheries sciences. This paper reviews and discusses some of the relevant themes, including accurate modelling via focused model selection techniques, dynamic goodness-of-fit testing of processes evolving over time, finding break points for phenomena experiencing regime shifts, prediction uncertainty, and optimal combination of information across diverse sources via confidence distributions. The methods are illustrated for the Hjort liver quality index time series. Its roots lie in the classic study by Hjort in 1914, where liver quality of the Northeast Arctic cod (Gadus morhua) for 1880–1912 was reported on and studied, along with related factors, making it one of the first teleost time series ever published. The series has been extended both backwards and forwards in time, to 1859–2012, due to comprehensive archival and calibration efforts of Kjesbu et al. in 2014, yielding one of the longest time series of marine science. Our study offers a detailed examination of this series and how it relates to and interacts with associated factors, including winter temperatures, length distribution parameters, cod mortality, and a certain index related to availability of food. We identify certain mild nonstationary aspects of the time series, show that there is a regime shift around 1990 in the ways the liver series interacts with winter temperatures, and demonstrate that mortality and food availability play important roles.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".