Characterizing polar mobilities to understand the role of weather, water, ice and climate (WWIC) information
Bibliographic record
Abstract
The Polar Regions are undergoing rapid environmental change while simultaneously witnessing growth and diversification of human activity. These changes call for more responsive, detailed and specialized weather, water, ice and climate (WWIC) information services so that the risks related to human activities can be minimized. Drawn from an extensive literature review this article provides an examination of selected sectors and their uses of WWIC information services in order to offer an initial understanding of diverse environmental forecasting needs. Utilizing a mobilities perspective we provide a characterization of mobility in the Polar Regions to help contextualize current WWIC uses and needs. Using four illustrative case studies of polar mobilities (community activities; cruise tourism; shipping; and government and research operations) the article explores two broad questions: (1) How are mobilities characterized in the Polar Regions? (2) What is known about the role of WWIC information in Polar mobilities? The findings suggest an incongruence between the information provided and the ways in which WWIC information is both used and needed by various sectors. Knowledge gaps are outlined that suggest more efforts are needed to understand the highly complex set of interconnections between WWIC users, providers, mobilities and decision-making across the Polar Regions.
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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 | 0.001 | 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".