Measuring and modeling of wetted surfaces
Bibliographic record
Abstract
This paper will present a comprehensive study on the measurement, modeling, and simulation of the optical properties of wet surface paints. Low observable paints are designed to camouflage the optical signature of a system by imitating the background thermal signature and scattering incident light (visible and IR). These properties are well studied for pristine conditions but their optical properties in real conditions, wet and at cold temperatures, are less known. Herein, we present an in-situ measurement of dry, wet, and icy paint samples commonly used for thermal signature management. The collected data is analyzed for input to ShipIR based on a derived nominal (diffuse) emissivity and specular reflectivity versus incidence angle using the Sanford-Robertson approximation, where the angular and spectral properties of surface reflectance are separable. ). A current and modified version of the ShipIR wetted surface reflectance model will be compared against the optical properties obtained by the SOC reflectometers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".