Spatial diversity of Pacific herring (Clupea pallasi) spawning areas
Bibliographic record
Abstract
Abstract Hay, D. E., McCarter, P. B., Daniel, K. S., and Schweigert, J. F. 2009. Spatial diversity of Pacific herring (Clupea pallasi) spawning areas. – ICES Journal of Marine Science, 66: 1662–1666. Eastern Pacific herring spawn in intertidal and shallow subtidal areas. Spawning sites are conspicuous: milt turns coastal waters white, sometimes for distances of many kilometres. This attribute has enabled biologists to document spawning distributions for more than 70 years throughout the 29 500 km coastline of western Canada. Spawning distributions and spatial diversity have varied over time. When aggregated over 70 years (1938–2007), spawning occurred along 5574 km or ∼20% of the total coastline. Cumulative annual spawn length ranges from 131 (in 1966) to 770 km (in 1992). We examined annual changes in spawn distribution using spatial units of variable size, ranging in area from a maximum of >1000 km2 to a minimum of <0.1 km2. Assessment of spatial diversity varied with the size of the spatial unit. Spatial diversity estimated from small spatial units (area <0.1 km2) was significantly correlated with spawning-stock biomass (SSB). In contrast, there was no correlation, and sometimes opposite temporal trends, between SSB and all larger spatial units (mean area >0.3 km2). The choice of spatial scale can affect the results from analyses of other factors, such as SSB, that could affect spatial diversity of spawning areas.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".