Bibliographic record
Abstract
Multi-year ice and fragments of icebergs can pose a danger to vessels and off-shore structures in ice infested polar seas. The risk of ice damage could be reduced if operators were able to detect and avoid hazardous ice in their path. Conventional marine radars are designed for target detection and avoidance. Smaller targets such as fragments of icebergs can get lost in the “sea clutter”. Detection on radar or visually can be as little as a half mile from a vessel, if at all. Digitally enhanced marine radars can provide a higher definition image of the ice that the vessel is transiting through and may help the user to identify certain ice features, but they cannot distinguish between old ice embedded in first-year ice. The Canadian Coast Guard (CCG) is working in partnership with Transport Canada and the Program of Energy Research and Development to develop an “Ice Hazard Radar”, a high-speed, cross-polarized, marine radar that will be able to differentiate between the various types of ice and will improve the detection of small hazardous targets in heavy sea conditions. This radar could assist vessels to navigate more efficiently through ice regimes by avoiding collisions with multi-year ice floes and fragments of icebergs, reducing risk of damage, transit time, fuel consumption, emissions and pollution in the vulnerable Arctic marine environment.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".