CFARS Site Suitability Initiative: An Open Source Approach to Evaluate the Performance of Remote Sensing Device (RSD) Turbulence Intensity Measurements & Accelerate Industry Adoption of RSDs for Turbine Suitability Assessment
Bibliographic record
Abstract
In this white paper, the results from the Consortium for Advancing Remote Sensing's (CFARS) Site Suitability Subgroup’s first benchmarking analysis of <em>unadjusted</em> RSD to cup anemometer turbulence intensity (TI) and reference cup-to-redundant cup measurement differences are presented. Eight organizations participated in the benchmarking activity, contributing a total of 35 datasets. This document introduces the Subgroup’s forthcoming analysis, examining the performance of <em>adjusted</em> RSD TI measurements compared to cup anemometry. This will be the industry’s first open source tool for comparing the performance of disparate RSD TI measurements and cup anemometry, the TI Adjustment Comparison Tool (TACT). TACT incorporates more than 15 adjustment techniques. Preliminary results from this benchmarking activity using TACT will be released to the industry in the Spring 2022 and summarized in detail in a forthcoming peer-reviewed article. Finally, to ensure the delivery of commercial value to open source science and tools generated in the Subgroup, a best practice collaboration framework to connect RSD TI benchmarking activities with RSD TI acceptance decision-making for site suitability assessment is introduced herein. The CFARS best practice framework provides a platform that enables data-driven decisions, on acceptable loads bias thresholds in a commercial setting. The framework encourages industry stakeholders to collaborate to further refine TACT, leverage the tool to advance industry understanding of the sensitivity of turbine fatigue load models to varying TI measurements and therefore de-risks the use of adjusted RSD measurements in site suitability assessment.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".