Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR
Bibliographic record
Abstract
Shallow lakes, with depths less than ca. 3.5–4 m, are a ubiquitous feature of the Arctic Alaskan Coastal Plain, covering up to 40% of the land surface. With such an extended areal coverage, lakes and their ice regimes represent an important component of the cryosphere. The duration of the ice season has major implications for the regional and local climate, as well as for the physical and biogeochemical processes of the lakes. With day and night observations in all weather conditions, synthetic aperture radar (SAR) sensors provide year-round acquisitions. Monitoring the evolution of radar backscatter (σ°) is useful for detecting the timing of the beginning and end of the ice season. Analysis of the temporal evolution of C-band σ° from Advanced Synthetic Aperture Radar (ASAR) Wide Swath and RADARSAT-2 ScanSAR, with a combined frequency of acquisitions from two to five days, was employed to evaluate the potential of SAR to detect the timing of key lake-ice events. SAR observations from 2005 to 2011 were compared to outputs of the Canadian Lake Ice Model (CLIMo). Model simulations fall within similar ranges with those of the SAR observations, with a mean difference between SAR observations and model simulations of only one day for water-clear-of-ice (WCI) from 2006 to 2010. For freeze onset (FO), larger mean differences were observed. SAR analysis shows that the mean FO date for these shallow coastal lakes is 30 September and the mean WCI date is 5 July. Results reveal that greater variability existed in the mean FO date (up to 26 days) than in that of melt onset (MO) (up to 12 days) and in that of WCI (6 days). Additionally, this study also identifies limitations and provides recommendations for future work using C-band SAR for monitoring the lake- ice phenology of shallow Arctic lakes.
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 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".