Wind energy resources exploitation and large-scale, non-grid-connection wind-powered industrial bases, construction in China Northwest Territories
Bibliographic record
Abstract
Northwest Territories in China is very rich in wind energy resources and has huge potential in development wind power, million kilowatts of wind power base is in planning and preparation, the wind power industry has entered a rapid and important development period. According to incomplete statistics, 2007, the wind farm of Built and under-construction are 23, the total installed capacity reached 1.4009 million kilowatts, and it is projected that the wind power installed capacity will reach 19.6892 million kilowatts in 2020, accounting for 38 percent of installed capacity of China. the paper reviewed the wind energy resources development research progress, analysis and assess the Northwest resources development and utilization of wind energy; identify the existing problems in the Northwest development of wind power industry; proposed wind energy resources development and the principle of non-grid wind power development goals In Northwest, using this as guide, integrate two national non-network and wind power and high energy-consuming non - Carbon-type industrial base: northern in Xinjiang net non-wind power and high energy-consuming industrial base and Jiuquan in Gansu net non-wind power and high energy-consuming industrial base, And layout ideas for each industrial base, put forward corresponding policies and measures of wind energy development and non-grid wind power industry base construction in final.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".