Inter-comparisons of Arctic snow depth products
Bibliographic record
Abstract
Applying ICESat-2 and CryoSat-2 freeboards, the Arctic snow depth (ISCS) was obtained from October 2018 to April 2022. The results were assessed using Operation Ice Bridge (OIB) and Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) in situ observations and compared with frequently used remote sensing (AMSR-E/AMSR2), climatology (modified W99 snow climatology data; MW99), and modelled (Pan-Arctic Ice Ocean Modelling and Assimilation Systems; PIOMAS) snow products. The ISCS snow depth exhibited high correlations (r = 0.67) and small mean bias (0.01 ± 0.06 m) against OIB results. We found good correlations (statistically significant) between MOSAiC and all snow depth products, except for MW99. Mean and absolute biases between ISCS and MOSAiC in situ observations were 0.01 ± 0.06 and 0.04 ± 0.04 m, respectively. The ISCS revealed clear spatial distribution from the Canadian Arctic Archipelago to open sea areas. In contrast, the PIOMAS snow depth revealed weak autumn variation, whereas the MW99 snow depth yielded distinct seasonal variability but a weak interannual change. Snow depth in October showed large differences among the various products, from which the AMSR topped the snow depth in the first-year ice region, while MW99 had the highest values for the multiyear ice zone.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".