A Case Study of Laser Wind Sensor Performance Validation by Comparison to an Existing Gage
Bibliographic record
Abstract
A case study concerning validation of wind speed measurements made by a laser wind sensor mounted on a 190 square foot floating platform in Muskegon Lake through comparison with measurements made by pre-existing cup anemometers mounted on a met tower on the shore line is presented. The comparison strategy is to examine the difference in measurements over time using the paired-t statistical method to identify intervals when the measurements were equivalent and to provide explanatory information for the intervals when the measurements were not equivalent. The data was partitioned into three sets: not windy (average wind speed measured by the cup anemometers ≤ 6.7m/s) windy but no enhanced turbulence (average wind speed measured by the cup anemometers > 6.7m/s), and windy with enhanced turbulence associated with storm periods. For the not windy data set, the difference in the average wind speeds was equal in absolute value to the precision of the gages and not statistically significant. Similar results were obtained for the windy with no enhanced turbulence data set and the average difference was not statistically significant (a=0.01). The windy with enhanced turbulence data set showed significant differences between the buoy mounted laser wind sensor and the on-shore mast mounted cup anemometers. The sign of the average difference depended on the direction of the winds. Overall, validation evidence is obtained in the absence of enhanced turbulence. In addition, differences in wind speed during enhanced turbulence were isolated in time, studied and explained.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".