Mapping weather, water, ice and climate (WWIC) information providers in Polar Regions: who are they and who do they serve?
Bibliographic record
Abstract
Environmental conditions in Polar Regions are becoming more dynamic due to climate change. As sea ice melts, the range of human activities in Polar Regions are projected to increase, while weather conditions are becoming more extreme and unpredictable. Provision and use of weather, water, ice and climate (WWIC) information plays a key role in ensuring that polar activities are conducted as safely as possible and can contribute to a reduction of the environmental footprint of human activities. In this article, we explore the WWIC information provider landscape in a polar context, drawing on a database we compiled to characterize the diversity of providers. The database is built on available literature and on an extensive desk-based research of WWIC information provider websites. We analyse the 374 providers categorized by (a) institutional background (public vs private), (b) the position of the provider relative to activities in the WWIC information space, and (c) the users they serve. While governmental institutions have a strong presence in information provision, new types of providers are now entering the scene. Scientific actors seem to play a substantial role as users as well as major providers of WWIC information services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".