Bibliographic record
Abstract
Wind resources are investigated and estimated offshore of the northern and central coasts of British Columbia, Canada. Remote sensing-based wind speed observations from a Synthetic Aperture Radar (SAR) mounted on the Canadian RADARSAT-2 satellite are used for mapping offshore winds. In addition, in-situ wind speed observations extracted from several buoys distributed in the study region are used to analyze the temporal and spatial wind speed variations in relation to wind power generation. Sustained winds above several wind turbine thresholds are analyzed and values of 50-yr and 100-yr return extreme wind speed levels are calculated. The wind variability analysis suggests few interruptions to power generation by either very low wind speeds or extreme wind speed events with high spatial variability between offshore areas and sites located within the coastal mountains. The SAR wind speed fields are characterized by a high spatial resolution but cover a period of less than 2.5 years with a random temporal availability. The SAR fields are extrapolated to reanalysis long-term wind fields that are available over a climatological time period with a sub-daily temporal resolution but a coarse spatial resolution. The extrapolation procedure is developed by applying a statistical downscaling model and a bias-based correction method. Wind fields from both methods are validated against the in-situ observations from buoys. The extrapolated wind fields are used for mapping offshore winds by creating a robust wind climatology that represents the mesoscale wind variance as well as the diurnal wind variability. This wind climatology is used to calculate the wind statistics and power density, in addition to estimate offshore wind resources. Viable areas for wind power development are defined by using high resolution bathymetric data and considering the general environmental and ecological constraints in the region. The estimated offshore wind resource energy using only theiv determined viable areas is found to resemble a large portion of the current total power generation in British Columbia. Most suitable areas for offshore wind farms are determined by developing criteria based on a combination of the turbine tower technology, water depth zoning and power density values.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".