Windpower Resource Screening for The Western U.S. Region*
Bibliographic record
Abstract
This article describes a comprehensive screening study performedin 2007 to identify wind energy resources in the 14-state Western ElectricCoordinating Council (WECC). WECC comprises the entire Western In-terconnection. With a footprint of 1.8 million square miles within the U.S.,two Canadian provinces, and Baja Norte, Mexico, WECC offers significantbut widely dispersed potential for farming wind resources. The methodol-ogy described in this article is novel but tested in application.Using resource maps of greatest wind potential, electric generationis incrementally increased to reach a regional 25% penetration target.This approach allows overloaded transmission corridors to be identi-fied that will require investment to reliably ship power to the areas ofgreatest demand growth. In this study, resolution is based on 1 km cells.Explicit consideration is given to reserve transmission capacity to esti-mate WECC’s ability to move power from remote sites. Wind resourceassumptions are based on National Renewable Energy Laboratory(NREL) wind maps, Class 3 or higher (mean annual wind speeds = 6.9m/s at 80 m). The wind resource is converted on the basis of generatingclusters of 77-meter diameter, 1.5-MWe turbines with a capacity factorof 48%. Limits are placed on distance to load centers to avoid transmis-sion congestion and to implicitly acknowledge an economic breakeventowards lower speeds and closer distance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".