Observing the snow and ice properties over the Labrador shelf with helicopter-borne Ground-Penetrating Radar, Laser and Electromagnetic sensors
Bibliographic record
Abstract
During March 2009, a unique data set was collected with helicopter-borne sensors from the Labrador shelf. For the first time a Ground-Penetrating-Radar (GPR) provided snow thicknesses and complemented the Electromagnetic-Laser (EM) and Video-Laser data sets to explain the ice and snow properties of the land-fast and mobile ice covers. Ice and snow thickness data were collected with helicopter-borne EM and GPR sensors along shoreward flight paths and video data with a video-laser system along seaward flight paths. A total of 550km of ice and snow thickness profile data was collected and a total of 550km of video data. As indicated by the RADARSAT-2 image, four distinct ice thickness regimes were seen. Offshore, small wave-broken floes existed with a very homogeneous 1.2m modal thickness. Inshore of this region, large floes were observed with the same modal thickness of 1.2m but having a larger thickness variability. Areas of open water and thin ice were seen offshore of the rough outer region of the land-fast ice. The GPR data showed that offshore the snow thickness appeared thinner as snow was continually blown from the ice into leads between the floes where it stimulated the formation of frazil ice.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".