Ice Sensing Technologies with Applications in Augmented Situational Awareness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper contains a literature review of technologies employed in the scientific literature to provide data on ice severity to augment situational awareness of human operators (aboard a ship or in a remote control center) and eventually autonomous navigation algorithms. As ships navigate in ice, masters use a wide source of information to assess the ice conditions along their planned route. This information is used to make ongoing assessments of the ice severity and to decide how to optimize the route to avoid damage to the ship, besetting, etc. Typically, this assessment is made by the officers in charge of the ship based on observations, experience, and metocean publications such as weather forecasts and ice charts. Significant levels of experience needed to safely assess and navigate in complex or severe ice conditions. A fundamental challenge in allowing autonomy or decision-support for navigation of ice-covered waters is providing accurate and relevant ice severity data that feeds decision-making.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it