Phenological and geographical patterns of walleye pollock (<i>Theragra chalcogramma</i>) spawning in the western Gulf of Alaska
Bibliographic record
Abstract
Over 20 years of egg sampling data were used to reconstruct the geographical and phenological patterns of walleye pollock (Theragra chalcogramma) spawning aggregations in the Gulf of Alaska (GOA). The analyzed time series (1972, 1978–1979, 1981–2000) included a documented event of climate change (i.e., 1988–1989) and the rise and fall of the GOA pollock population abundance and harvests. We compared results from two generalized additive model (GAM) formulations: one assuming no change of egg distribution and phenology over the examined time series (stationary) and the other admitting such changes (nonstationary) across an epoch determined from the data. Results from both model formulations corroborate the existence of a high egg concentration in Shelikof Strait, historically the primary spawning area of pollock in the GOA. However, model results also highlight the presence of other secondary, and possibly transitory, centers of egg distribution at various locations along the shelf and slope regions of the GOA. In addition, results from the nonstationary (and statistically superior) formulation indicate that the abundance of the non-Shelikof aggregations has increased over time, along with a tendency for earlier occurrence and displacement toward shallower areas of the high egg density regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".