Bibliographic record
Abstract
This paper presents the development and assessment of a numerical method for simulated site calibration. The wind flow over complex terrain is predicted with a small length scale resolution. The flow field is resolved with the Reynolds averaged Navier–Stokes equations, complemented by the k‐ϵ turbulence model, with special treatment of the ground boundary to account for very large roughness lengths such as forest. The computational model is solved using FLUENT. A complex site, Riviere au Renard, located in Gaspesie, QC, Canada, has been selected and data have been collected from five met masts installed on this site. An experimental data analysis has been undertaken with emphasis on uncertainty evaluation. Three sets of results are presented. First, the numerical method is validated over flat terrain by comparing the simulation results with Monin–Obukhov similarity theory. Second, the assessment of the numerical method over complex terrain is done by comparing the wind velocity profiles at three of the met masts for three different wind orientations. Finally, traditional and numerical site calibrations for Riviere au Renard are presented for two wind directions. The numerical results are within the experimental data uncertainty.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".