SAR-Satellite for Offshore and Coastal Wind Resource Analysis, with Examples from St. Lawrence Gulf, Canada
Bibliographic record
Abstract
This paper illustrates the benefits of using remote sensing methodology as an intermediate step to assess offshore and coastal wind resources. Results are based on an ongoing research to understand wind patterns in the St-Lawrence Gulf. This area combines two advantages for wind power development in Canada: a) very good wind, b) high potential of the large scale integration of wind power with the hydro-wind concept. Advantages and drawbacks of satellite techniques in such a complex environment are reviewed. Our approach of satellite data selection for dominant wind conditions reduces the weakness of Synthetic Aperture Radar (SAR) satellite temporal resolution. Wind fields are extracted from sixteen scenes provided by RADARSAT-1. Results are compared with two main sources: in situ measurements and QuickSCAT scatterometer computations. Among interesting findings, it appears that a relative small sample of scenes can already indicates the best wind sites to be investigated for further analysis. The proposed approach to obtain a global wind map of the gulf and the advantages of such high-resolution wind maps to wind resource assessment are discussed.
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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 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.002 |
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".