Assessment of Lead Discrimination from CryoSat-2
Bibliographic record
Abstract
Sea ice is strongly affecting the global climate, and the sea ice extent has been monitored by satellites since 1979. To estimate the Arctic sea ice volume, ice thickness must be determined. The measurements of sea ice thickness are however more difficult to achieve, and encounter limitations due to spatial and temporal variability. The measurements of sea ice freeboard may be used to estimate sea ice thickness, when combined with examination of leads between ice floes to determine the local sea surface height. With CryoSat-2 (CS), we have the opportunity to measure much more of the Arctic Ocean due to its high sampling rate and geographical coverage to 88 oN/S. Validation of the CS retrievals are very important to verify the derived sea ice thickness and understand the associated error sources. We present a comparative analysis of CryoSat-2 elevations with the Operation IceBridge Airborne Topographic Mapper (ATM) laser altimeter data gathered on April 2, 2012, where the NASA P-3 completed an underflight of CS orbit number 10520, north of Alert, Nunavut, Canada. We present a new lead detecting algorithm which was developed using the CS Level1b (L1b) waveforms, and we analyze its capabilities via comparisons with IceBridge imagery and ATM elevations. In addition,using CS L1b waveforms we have developed a method to find misplaced CS Level 2 elevations and correct them to remove any elevation bias.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".