Wind dataset assessment and energy estimation for potential future offshore wind farm development areas on the Scotian Shelf
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. The Scotian Shelf is one of the top wind regions in the world. To assess the wind energy potential on the shelf, in this study we first assessed the uncertainties of four commonly used wind datasets – ERA5, CFSv2, NARR, and HRDPS – by comparing them against observational wind data from both nearshore and offshore sites. The assessment showed that the root mean square error (RMSE) of the datasets ranged from 1.6 to 2.4 m s−1 in wind speed and from 24.6 to 36.4° in wind direction. HRDPS performed best at the nearshore sites, while ERA5 was more accurate at the offshore sites. We then estimated the wind energy potential of six potential future development areas (PFDAs) on the shelf using ERA5. The estimates showed that wind energy varied seasonally, with summer wind energy production being 34 %–40 % lower than in winter. The uncertainties in wind datasets amplified the variation in wind energy production by up to 28 % in winter and 50 % in summer. The energy output was sensitive to turbine spacing due to wind wakes, which reduced energy production by 19 %–30 % in winter and 37 %–46 % in summer, depending on the configuration of wind speeds, wind directions, and the specific layout of the wind farms. This strong variation in wind energy output suggests that a more feasible operational method should be used to balance energy production and usage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it