The Use of Weather, Water, Ice, and Climate (WWIC) Information in the Polar Regions: What Is Known after the Decadelong Polar Prediction Project?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The polar regions are facing a wide range of compounding challenges, from climate change to increased human activity. Infrastructure, rescue services, and disaster response capabilities are limited in these remote environments. Relevant and usable weather, water, ice, and climate (WWIC) information is vital for safety, activity success, adaptation, and environmental protection. This has been a key focus for the World Meteorological Organization’s (WMO) Polar Prediction Project (PPP), and in particular its “Societal and Economic Research and Applications” (PPP-SERA) Task Team, which together over a decade have sought to understand polar WWIC information use in relation to operational needs, constraints, and decision contexts to inform the development of relevant services. To understand research progress and gaps on WWIC information use during the PPP (2013–23), we undertook a systematic bibliometric review of aligned scholarly peer-reviewed journal articles ( n = 43), examining collaborations, topics, methods, and regional differences. Themes to emerge included activity and context, human factors, information needs, situational awareness, experience, local and Indigenous knowledge, and sharing of information. We observed an uneven representation of disciplinary backgrounds, geographic locations, research topics, and sectoral foci. Our review signifies an overall lack of Antarctic WWIC services research and a dominant focus on Arctic sea ice operations and risks. We noted with concern a mismatch between user needs and services provided. Our findings can help to improve WWIC services’ dissemination, communication effectiveness, and actionable knowledge provision for users and guide future research as the critical need for salient weather services across the polar regions remains beyond the PPP. Significance Statement Every day, people in the Arctic and Antarctic use weather, water, ice, and climate information to plan and carry out outdoor activities and operations in a safe way. Despite advances in numerical weather prediction, technology, and product development, barriers to accessing and effectively communicating high-quality usable observations, forecasts, and actionable knowledge remain. Poorer services, prediction accuracy, and interpretation are exacerbated by a lack of integrated social science research on relevant topics and a mismatch between the services provided and user needs. As a result, continued user engagement, research focusing on information use, risk communication, decision-making processes, and the application of science for services remain highly relevant to reducing risks and improving safety for people living, visiting, and working in the polar regions.
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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 | 0.001 | 0.002 |
| 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 it