Effects of radar side-lobes on snow depth retrievals from Operation IceBridge
Bibliographic record
Abstract
Abstract Arctic snow depth data products from four years (2009–12) of Operation IceBridge (OIB) surveys are examined. In our analysis, we found spurious spikes in the snow depth distributions of both the multi-year and seasonal ice covers. These spikes are artifacts that stem from the incorrect identification of side lobes and main lobes of the impulse response of the snow radar as returns from the air–snow interface. The current OIB snow depth retrieval algorithm does not explicitly account for the presence of these side lobes and main lobes. As a result, overall accuracy of snow depth returns and related statistics is negatively affected. Although the range locations of these side lobes are predictable for each radar installation, they vary with individual airborne campaigns. Comparisons with limited in situ snow surveys show significant differences of >20 cm between OIB and in situ snow surveys. These artifacts affect OIB ice thickness estimates because they rely on estimates of sea-ice freeboard, which are calculated as the differences between coincident snow freeboard from lidar elevations and the retrieved snow depth estimates discussed here. Since these products are widely distributed to the scientific community, our results suggest that earlier geophysical studies based on these products may need to be re-examined.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".