Role of disease in abundance of a Pacific herring (<i>Clupea pallasi</i>) population
Bibliographic record
Abstract
Disease significantly affects population abundance of Pacific herring (Clupea pallasi). Comprehensive epidemiological study of the Pacific herring population of Prince William Sound, Alaska, U.S.A., from 1994 to 2000 included complete necropsy examination of 230500 fish each spring and 40160 fish each fall (total n = 2983 fish). Mortality is best estimated, through modifications of an age-structured assessment model, using a disease index that combines the prevalence of viral hemorrhagic septicemia virus (VHSV) with the prevalence of ulcers. Risk factors for an epidemic include poor body condition and abundant recruitment before spawning in the spring. Prevalence of the pathogen Ichthyophonus hoferi increased as fish aged, but changes in I. hoferi prevalence were not related to changes in population abundance. Disease that caused an epidemic in 1998 (VHSV and ulcers) nearly disappeared from the population when changes in abundance were detected by traditional stock assessment methods in 1999. Disease significantly affects recruitment the two lowest recruitment estimates on record, in 1994 and 1999, followed increased natural mortality of adults in 1993 and 1998.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".