Bibliographic record
Abstract
Abstract Accurate and reliable surveillance and forecasting of environmental conditions are necessary for safe and efficient oil and gas activities both onshore and offshore. In the Arctic, environmental challenges include seasonal sea ice and low temperature extremes. In the absence of pooled forecasting services and operational-grade forecasting capacity by public weather services, Shell has developed and operates an in-house, Anchorage based forecasting program designed specifically for the demands and requirements of Shell's Alaska operations. The Shell Ice and Weather Advisory Center (SIWAC), now in its eighth year of operation, has evolved to be the most comprehensive and focused ice and weather forecast operation covering the offshore and coastal areas from the Gulf of Alaska to the Canadian Beaufort Sea. SIWAC consists of a team of fulltime Arctic-experienced forecasters working in a 24/7 rotation schedule and are fully integrated into the operations process, directly engaging with field personnel and decision makers. Development of differentiating forecast products and services depends not only on an expert team, but also a robust observation program consisting of contracted and public satellite imagery, a network of Metocean buoys, satellite-tracked ice movement beacons, and steady stream of field observations from specially trained personnel aboard marine and aviation assets. In 2011, Shell entered into a Memorandum of Agreement with the US National Oceanographic and Atmospheric Administration that described a framework for collaboration, communication, and information sharing between the Agency and Industry. This agreement leverages the strengths of each party and opens Shell's Arctic ice and Metocean data for use within NOAA forecasting offices, numerical model ingestion, climate research, and general public consumers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".