Thin ice thickness distribution and ice production in the North Water and Laptev Sea polynya regions using MODIS thermal infrared imagery
Bibliographic record
Abstract
We present investigations of Arctic polynya dynamics for the period 2002/2003 to 2011/2012. Thin ice thicknesses were calculated from MODIS ice surface temperatures, combined with ECMWF ERA-Interim reanalysis atmospheric data in an energy balance model. Regions of interest include the North Water Polynya, located between Ellesmere Island (Canada) and Greenland, and the Laptev Sea flaw polynyas. Based on calculated thin ice thicknesses, associated quantities like polynya area and total ice production were derived for all regarded regions and compared to recent studies using passive microwave remote sensing data. Calculated ice production reaches mean values of 223 km3 for the North Water Polynya and 79 km3 for the Laptev Sea. They underline the importance of the two coastal polynya systems in the context of the Arctic sea ice budget, although their individual contribution seems to be overestimated in other satellite-based studies. For both regions, obtained polynya areas and ice production clearly exceeded the corresponding values from passive microwave studies, despite a good agreement in the overall seasonal development. Possible reasons include a hidden effect of undetected clouds and the applied parametrizations in the polynya area retrieval. The application of a simple cloud coverage-correction scheme yielded reasonable adjustments for the polynya area and accumulated ice production, while open questions originating from inherent cloud effects have to be addressed in future studies. Noticeably, the sea ice cover in both regarded polynya regions shows signs of a delayed fall freeze-up over the 10 year-period.
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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 | 0.000 | 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) | 0.000 | 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".