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
The RADARSAT program takes its origin in the seventies when the Canadian Government was seeking a reliable technology to ensure safe navigation through sea ice. At that time there was an expectation that it would be necessary for large tanker to navigate through the Beaufort Sea to transport oil extracted from drilling platforms. During the same period the Canadian Center for Remote Sensing was developing SAR applications using an airborne SAR instrument on a Convair 580. One of these applications of great interest for the Canadian Ice Service was ice discrimination provided by the C-band SAR data to support the development of ice map to guide navigation in winter in the Gulf of Saint-Lawrence Seaway. With the combination of both, interest for oil transportation in the Beaufort Sea and the need for accurate and frequent ice map the business case for RADARSAT- 1 was born. This paper provides an overview on the RADARSAT program since its beginning and is partially based on a presentation delivered in October 2013 at the Canadian Space Agency on the motivation and evolution of the RADARSAT program [1].
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 | 0.001 |
| 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