Model-Based Projections and Uncertainties of Near-Surface Wind Climate in Western Canada
Bibliographic record
Abstract
Abstract Near-surface wind is important in forestry, agriculture, air pollution, building energy use, and wind power generation. In western Canada it presently plays a minor role in power generation, but ongoing reductions in the cost of wind power infrastructure and the increasing costs of conventional power generation (including environmental costs) motivate the assessment of the projected future wind climate and uncertainties in this projection. Multiple realizations of the Canadian Regional Climate Model (CRCM) at 45-km resolution were driven by two global climate models over the periods 1971–2000 (using historical greenhouse gas concentrations) and 2031–60 (using the SRES-A2 concentration scenario). Hourly wind speeds from 30 stations were analyzed over 1971–2000 and used to calibrate downscaled ensembles of projected wind speed distributions over 2031–60. At most station locations modest increases in mean wind speed were found for a majority of the projections, but with an ensemble spread of the same order of magnitude as the increases. Relative changes in mean wind speeds at station locations were found to be insensitive to the station observations and calibration technique. In view of this result, projected relative changes in future wind climate over the entire CRCM domain were estimated using uncalibrated pairs of past-period and future-period wind speed distributions. The relative changes are robust, in the sense that their ensemble mean relative change is greater than their standard deviation, but are not very substantial, in the sense that their ensemble mean change is generally less than the standard deviation of their annual means.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".