Monitoring Ice Phenology and Characteristics in Mid-latitudes using RADARSAT-2
Bibliographic record
Abstract
This study investigates the use of remote sensing for monitoring ice phenology and ice characteristics (ie. ice thickness). The primary data used were RADARSAT-2 images acquired over Central Ontario between 2008 and 2017. In order to monitor ice phenology, an automated threshold method was developed to identify freeze and melt events. During the 2015/2016 and 2016/2017 ice season 12 out of 17 identified freeze events and 13 out of 17 identified melt events were successfully validated. The radar determined dates were validated using in situ data, and visible remote sensing data. Temperature models and radar backscatter were used to estimate ice thickness in Central Ontario. The results of this analysis were validated using a combination of in situ measurements and data from a Shallow Water Ice Profiler (SWIP), correlation statistics for temperature models were >0.9 and an R2 of 0.6 was observed for backscatter models.
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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".