Sub-Arctic no more: Short- and long-term global-scale prospects for snow crab (Chionoecetes opilio) under global warming
Bibliographic record
Abstract
Snow crab is a sea-ice associated species that supports several economically important fisheries in northern latitudes. During the past decade considerable stock range changes have occurred, characterized by a general shift from sub-Arctic ecosystems into the Arctic. We developed predictive models for short-term biomass trajectories and long-term habitat potential under a changing climate. Sea ice extent and the Arctic Oscillation were important variables in the short-term models. Future sea ice extent was used as an analog for long-term habitat potential and was predicted as a function of projected atmospheric carbon dioxide concentrations and the Arctic Oscillation. Our results show that global scale snow crab habitat and biomass are currently at or near historically measured highs. Similar overall habitat potential to historic and current levels is expected to continue out to 2100 under best case CO2 scenarios but declines below historic levels are projected to begin after about 2050 under worst cast CO2 scenarios. In the short-term, most historical stock ranges are expected to maintain productive fisheries while new habitats open. In the long-term, under all CO2 scenarios, we project a shift in habitats from historic ranges into new frontiers as sea ice recedes. Future population trajectories depend upon the ability of snow crab to track habitat shifts and we discuss possible forthcoming changes in context of potential socioeconomic outcomes.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".