Tailored Investments Needed to Support Weather, Water, Ice, and Climate Services in the Polar Regions
Bibliographic record
Abstract
Abstract The Polar Prediction Project (PPP), one of the flagship programs of the World Meteorological Organization’s (WMO) World Weather Research Programme (WWRP), has come to an end after a decade of intensive and coordinated international observing, modeling, verification, user engagement, and education activities. While PPP facilitated many advancements in modeling and forecasting, critical investment is now required to turn prediction science into salient environmental services for the polar regions. In this commentary, the members of the Societal and Economic Research and Applications task team of PPP, a group of social scientists and service delivery specialists, identify a number of insights and lessons that are critical for the implementation of the follow-up program Polar Coupled Analysis and Prediction for Services (PCAPS). We argue that in order to raise the societal value of polar environmental services, we need to better understand the diversity of highly specific user contexts; to tailor the actionability of weather, water, ice, and climate (WWIC) service development in the polar regions through inclusive transdisciplinary approaches to coproduction; to assess the societal impact of improved environmental services in the polar regions; and to invest and provide dedicated funding for involving the social sciences in research and tailoring processes across all the polar regions.
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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 | 0.005 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".