Design and Development of a Greenland Ice and Metocean Geoportal
Bibliographic record
Abstract
Abstract Completed and planned metocean and ice measurement programs off Greenland's eastern and western coasts result in large and varied datasets characterizing physical phenomena such as icebergs, sea ice, seabed scours, weather, surface waves, ocean currents, and water properties. Future planned measurement programs will expand on the spatial and temporal breadth of these datasets. Other datasets support the analysis of the measurement data including license area locations, bathymetry, glacier calving areas, and notable submarine features. In order to plan measurement programs, manage the acquired datasets, and use the data for characterization of the physical environment, a web-based geoportal was designed and developed. The geoportal aids scientists and engineers in their discovery and use of metocean and ice data. The geoportal development required balancing two aspects. Firstly, scientists and engineers have extensive needs to upload, organize, search, visualize, analyze, and download large and varied datasets. Secondly, there are inherent limitations of web technologies due to bandwidth, latency, and security constraints. Many design decisions were focused on balancing these issues and are presented here.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".