Airborne surface roughness and sea ice thickness measurements during CCGS Henry Larsen cruise: Canadian Arctic Through flow (CATs) 2009
Bibliographic record
Abstract
Helicopter-borne laser profiling of sea ice surface roughnessLaser profiler measurements were performed during the CCGS Henry Larsen cruise: Canadian Arctic Through flow (CATs) 2009 cruise to Nares Strait. Airborne surveys were conducted on August 16, 18 and 19, 2009.A laser was mounted on a helicopter pointing vertically downwards to measure the altitude above the ice surface which nominally was 30 m. Depending on flight speed, the spatial sampling interval ranged between 0.02 and 0.15 m. Positioning of the profiles was performed by means of a Global Positioning System (GPS).After eye inspection of the data and removal of outliers, the low frequency helicopter motion is eleminated from the data using a multiple filter procedure described by Hibler (1972, doi:10.1029/JC077i036p07190), Dierking (1995, doi:10.1029/94JC01938), and Haas et al. (1998, doi:10.1016/S0165-232X(97)00019-0). It takes advantage of the fact that the helicopter height variations are only at low frequencies, whereas the surface roughness is a superimposed, high frequency signal. The resulting ice morphology is obtained relative to the surface of the surrounding level ice. Absolute freeboard, i.e. the height of the surface above the water level, cannot be obtained unless the helicopter height variations are independently determined by means of differential GPS and Inertial navigation systems. The resulting surface profiles can be used to identify pressure ridges, e.g. by a Rayleigh criterion. By this criterion only local maxima which are twice as high as the surrounding local minima are defined as pressure ridges.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".