Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean
Bibliographic record
Abstract
Abstract Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific “answers,” but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".