Spatiotemporal modeling of mature‐at‐length data using a sliding window approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Assessing maturity status of fish and invertebrate species is important for understanding population dynamics with results (e.g., estimates of reproductive potential) often used to inform fisheries management strategies (e.g., the setting of minimum legal size requirements for fishing). Maturity rates may vary substantially across a population's range, as well as between years. In addition, maturity data are typically obtained from fisheries‐independent surveys that may be incomplete (or missing) from year to year. Here we propose a spatial generalized linear mixed model (GLMM) framework for maturity data that includes spatially correlated random effects to address variations in space, and a sliding window approach to deal with unbalanced maturity data in both space and time. We demonstrate, with both real data and a simulation study, that this combined approach results in unbiased estimates of important growth parameters. Results of using our spatial GLMM framework with Greenland halibut ( Rheinhardtius hippoglossoides ) mature‐at‐length data from surveys of the eastern Canadian Arctic show that females mature at a much larger size than do males. The length at which 50% of the stock is mature () is found to be higher in Baffin Bay compared to Davis Strait, and a declining trend in the in recent years is revealed for both sexes. Our proposed methodology extends far beyond our current application in being useful for analyzing unbalanced spatiotemporal data from an array of diverse scientific fields.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it