Bibliographic record
Abstract
There is a strong public and political desire to incorporate wind power in most grids including that of Manitoba Hydro. This paper describes Manitoba Hydro's plans to incorporate wind power over the next 20 years and the associated wind integration costs. Since Manitoba Hydro currently has excess hydro power that supplies a domestic load of approximately 20 TWh/year and 10 TWh/year of export power sales, any new power source (hydro, wind, etc.) added between now and 2020–2024 must be sold on the export power market. It is for this reason that Manitoba Hydro must pay “great” attention to the costs associated with purchasing and integrating locally produced wind power before reselling it on the export market. Manitoba Hydro desires to pass on the maximum value to wind developers less its direct cost such as the shaping and firming costs and wind integration costs. The Manitoba Hydro wind integration costs are considered specific to Manitoba Hydro, since the ability to provide reserves and the Manitoba Hydro response to hydraulic inefficiencies is unique to Manitoba Hydro. The wind integration impacts and related costs were studied over low, medium, and high water supply conditions. Additional reserves are required because of wind volatility and wind generation forecast error. Reductions in wind generation are of the most concern because other generation must be increased to counter balance the shortfall in wind generation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".