Towards Arctic Resource Governance of Marine Invasive Species
Bibliographic record
Abstract
Scientific and policy-oriented publications highlighting the magnitude of uncertainty in the changing Arctic and the possibilities for effective regional governance are proliferating, yet it remains a challenging task to examine Arctic marine biodiversity. Limited scientific data are currently available. Through analysis of marine invasions in the Arctic, we work to identify and assess patterns in the knowledge gaps regarding invasive species in the Arctic that affect the ability to generate improved governance outcomes. These patterns are expected to depend on multiple aspects of scientific research into invasive species threats in the Arctic, including the ways in which known marine invasions are related to different stakeholder groups and existing disparate national and international experiences with invasive species. Stakeholdergroups include dominant industries (fishing, shipping, tourism, resource exploration) and indigenous communities (regarded as resource users, citizen scientists, and recipients of goods shipped from other locations). Governance gaps are examined in the context of applied national policies (such as promoting or intercepting intentional introductions), international agreements (regarding introductions and mitigations) and existing prevention programs (regional, national and international). We intend to help focus domestic and international governance and research initiatives regarding introduced species on the most valuable, cost effective options, given the knowledge gaps derived from systematic research limitations and opportunities in the Arctic environment.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".