Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and PIOMAS Data
Bibliographic record
Abstract
In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridge aircraft flights, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). The satellite products are based on three different retrieval methods: an energy budget approach, measurements of ice freeboard, and the relationship between passive microwave brightness temperatures and thin ice thickness. Inter-comparisons are done for the periods of overlap from 2003 to 2013. Results show that ICESat sea ice is thicker than APP-x and PIOMAS overall, particularly along the north coast of Greenland and Canadian Archipelago. The relative differences of APP-x and PIOMAS with ICESat are −0.48 m and −0.31 m, respectively. APP-x underestimates thickness relative to CryoSat-2, with a mean difference of −0.19 m. The biases for APP-x, PIOMAS, and CryoSat-2 relative to IceBridge thicknesses are 0.18 m, 0.18 m, and 0.29 m. The mean difference between SMOS and CryoSat-2 for 0~1 m thick ice is 0.13 m in March and −0.24 m in October. All satellite-retrieved ice thickness products and PIOMAS overestimate the thickness of thin ice (1 m or less) compared to IceBridge for which SMOS has the smallest bias (0.26 m). The spatial correlation between the datasets indicates that APP-x and PIOMAS are the most similar, followed by APP-x and CryoSat-2.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".