Interim guidelines for operational implementation of SAR applications for lake ice monitoring and mapping: break-up and freeze-up
Bibliographic record
Abstract
Preface Lake ice represents an important component of Canadian landscape and influences hydrological, climatic, biological, cultural and economic systems. The timing of freezeup and break-up affects all of these systems. Within the Government of Canada, monitoring of lake ice freeze-up is of operational interest to Environment Canada and Parks Canada Agency. Remote sensing methods are required for monitoring large and remote geographical areas, and Synthetic Aperture Radar (SAR) capabilities are needed to operate during winter darkness and persistent cloud cover. This report describes methods for the monitoring of lake ice freeze-up and break-up with the help of images from Canada's RADARSAT-2 satellite and provides guidance for the operational implementation of these methods with the Government of Canada. For information please contact: J.J. van der Sanden, Natural Resources Canada, Canada Centre for Mapping and Earth Observation, joost.vandersanden@nrcan-rncan.gc.ca.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.053 |
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".