Using Synthetic Aperture Radar to Define Spring Breakup on the Kuparuk River, Northern Alaska
Bibliographic record
Abstract
Spring runoff measurements of Arctic watersheds are challenging given the remote location and the often dangerous field conditions. This study combines remote sensing techniques and field measurements to evaluate the applicability of synthetic aperture radar (SAR) to defining spring breakup of the braided lower Kuparuk River, North Slope, Alaska. A statistical analysis was carried out on a time series (2001–10) of SAR images acquired from the European Remote-Sensing Satellite (ERS-2) and the Canadian RADARSAT satellite, as well as on measured runoff. On the basis of field information, the SAR images were separated into pre-breakup, breakup, and post-breakup periods. Three variables were analyzed for their suitability to bracket the river breakup period: image brightness, variance in brightness over the river length, and a sum of rank order change analysis. Variance in brightness was found to be the most reliable indicator. A combined use of that variance and sum of rank order change appeared promising when enough images were available. The temporal resolution of imagery served as the major limitation in constraining the timing of the hydrologic event. Challenges associated with spring runoff monitoring and the sensitive nature of SAR likely resulted in an earlier detection of surficial changes by the remote sensing technique compared to the field runoff observations. Given a sufficient temporal resolution, SAR imagery has the potential to improve the spatiotemporal monitoring of Arctic watersheds for river breakup investigations.
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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 | 0.001 |
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".