An Empirical Analysis of Bulk Cn2 Models over Water
Bibliographic record
Abstract
Abstract This article examines some of the difficulties associated with the determination of C2n over water in a coastal region using a bulk model. The analysis shows the need to supplement bulk models with elements that do not belong to traditional Monin–Obukhov surface-layer theory. A reexamination of the scintillation measurements collected during the Electro-Optical Propagation Assessment in a Coastal Environment (EOPACE) campaign leads the authors to include 1) the nonuniformity of sensible heat and humidity fluxes and 2) a deficit of the scintillation that seems to depend only on the characteristic virtual potential temperature. It is suggested that the anomalous scintillation deficit represents an alteration of a characteristic length related to the optical turbulence, likely caused by the interaction of the surface layer with the sea surface. The new parameters are estimated using Bayesian regression methods applied to the EOPACE data. Predictions obtained with this new model are then compared with scintillation measurements obtained during the recent Validation Measurement for Propagation in the Infrared and Radar (VAMPIRA) campaign. Better agreement is obtained with the new model than with a conventional bulk model. The implications of the modifications made to the calculation of C2n are discussed.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".