Applying triple collocation for verifying wind resource measurements and reanalysis data
Bibliographic record
Abstract
Abstract Dual comparisons or dual collocation, respectively, is a standard approach in wind data analysis, and particularly in the wind energy resource assessment context, for comparing and relating two time series from different sources describing the same quantity as e.g. the wind speed at a specific height over a certain period of time. This comparison can be used to validate a data source, or to derive a correction for one of the two datasets. Though being widely used, dual collocation comes with inherent flaws which are partly unknown or ignored. A major problem is that dual collocation requires the definition of a reference in advance. Triple collocation may instead allow for a more objective and comprehensive evaluation, where (three) suitable data sources are available, and possibly help to identify which datasets are most suited to represent the relevant wind resource. In this study, we apply the triple collocation methodology to two typical wind resource assessment test cases. Results are promising in providing more information on the analysed datasets and improving the use of different types of data for wind resource assessment in the future.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".