Combining TerraSAR-X and time-lapse photography for seasonal sea ice monitoring: the case of Deception Bay, Nunavik
Bibliographic record
Abstract
Abstract. This article presents a case study for the combined use of TerraSAR-X and time-lapse photography time series in order to monitor seasonal sea ice processes in Nunavik's Deception Bay. This area is at the confluence of land use by local Inuit, ice-breaking transport by the mining industry, and climate change. Indeed, Inuit have reported greater interannual variability in seasonal sea ice conditions, including later freeze-up and earlier breakup. Time series covering 2015 to 2018 were acquired for each data source: TerraSAR-X images were acquired every 11 d, and photographs were acquired hourly during the day. We used the combination of the two time series to document spatiotemporal aspects of freeze-up and breakup processes. We also report new X-band backscattering values over newly formed sea ice types. The TerraSAR-X time series further show potential for melt and pond onset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".