Is climate change contributing to range reductions and localized extinctions in northern (Haliotis kamtschatkana) and flat (H. walallensis) abalones?
Bibliographic record
Abstract
Abalone abundance surveys from the 1970s were repeated 30 yrs later following a period of increased sea surface temperatures along the Pacific coast of the United States.Northern abalone, Haliotis kamtschatkana (Jonas, 1845) once abundant enough to support commercial fishing in Washington and Canada, are now extremely rare in the southern portion of their range in southern and central California.They have also declined 10 fold in northern California in the absence of human fishing pressure.In Washington, northern abalone are in decline and exhibit recruitment failure despite closure of the fishery.Flat abalone, Haliotis walallensis (Stearns, 1899) no longer occur in southern California, and in central California have declined from 32% to 8% of the total number of abalones, Haliotis spp., inside a marine reserve.The distribution of flat abalone appears to have contracted over time such that they are now only common in southern Oregon where they are subject to a new commercial fishery.Given these range reductions, the long-term persistence of flat abalone and northern abalone (locally) is a concern in light of threats from ocean warming, sea otter predation, and the flat abalone fishery in Oregon.The likelihood of future ocean warming poses challenges for abalone restoration, suggesting that improved monitoring and protection will be critical, especially in the northern portions of their distributions.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".