Increased harbor porpoise mortality in the Pacific Northwest, USA: understanding when higher levels may be normal
Bibliographic record
Abstract
In 2006, a marked increase in harbor porpoise Phocoena phocoena strandings were reported in the Pacific Northwest of the USA, resulting in the declaration of an unusual mortality event (UME) for Washington and Oregon to facilitate investigation into potential causes. The UME was in place during all of 2006 and 2007, and a total of 114 porpoises stranded during this period. Responders examined 95 porpoises; of these, detailed necropsies were conducted on 75 animals. Here we review the findings related to this event and how these compared to the years immediately before and after the UME. Relatively equal numbers among sexes and age classes were represented, and mortalities were attributed to a variety of specific causes, most of which were categorized as trauma or infectious disease. Continued monitoring of strandings during 4 yr following the UME showed no decrease in occurrence. The lack of a single major cause of mortality or evidence of a significant change or event, combined with high levels of strandings over several post-UME years, demonstrated that this was not an actual mortality event but was likely the result of a combination of factors, including: (1) a growing population of harbor porpoises; (2) expansion of harbor porpoises into previously sparsely populated areas in Washington's inland waters; and (3) a more well established stranding network that resulted in better reporting and response. This finding would not have been possible without the integrated response and investigation undertaken by the stranding network.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".