An Integrated Spatial Information System for Ice Service, The 20th
Bibliographic record
Abstract
The Canadian Ice Service (CIS) of Environment Canada is responsible for providing sea ice and iceberg information under the Canadian Federal Ice Program. The mandate of the Canadian Ice Service is to provide timely information on ice conditions for navigational purposes, to warn marine operators of hazardous ice conditions, and to maintain a general historic knowledge of ice conditions and ice climatology. This paper presents the mission-critical application of Remote Sensing and GIS technology in the Canadian Ice Service for supporting the marine community. The products are generated on a daily basis and products turnaround time is 24 hours. A typical day for our operation involves the real-time data acquisition, processing, and analysis of remotely sensed geographic data; the integration of vector, raster, and alphanumeric information; generation and dissemination of text, chart, image and spatially enabled products to clients and partners; clients making their decisions based on the information received from CIS. All products are deposited in a central repository and are made available to the public and partners via a geo-spatial web service.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.077 |
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".