Measuring and modelling of wetted surfaces (2.0)
Bibliographic record
Abstract
This paper is a follow up study to our previous work focused on the measurement, and modeling of optical properties of a wet painted surface. Our previous study attempted to replicate a 2008 commercial (unclassified) dark navy grey paint used on the Canadian Research Vessel (CFAV Quest) for water film cooling experiments. Those results revealed that solar absorptivity (a function of the pigment size distribution) can vary markedly between different types of metallic paints offered by the same supplier. The first part of this new study will compare three different paint types from the original supplier (Interlac 665, Interfine 979, Interthane 990) with the hope of identifying which of these match the one used in the 2008 CFAV Quest experiments. Low solar absorptive (LSA) paints are often used to lower the air conditioning load of a naval ship and also lower its thermal infrared signature. These properties are well studied for pristine conditions but their optical properties in real conditions, wet and at cold temperatures, are less known. The original study presented in-situ measurement of dry, wet, and icy paint samples using one of the paint types (Interlac 660). The same measurements will be conducted for all three paint types and compared. The instruments at Surface Optics Corporation measure the specular/diffuse hemispherical directional reflectance from 250 nm to 25 μm and the bidirectional reflectance from 4 to10μm to reveal distinct optical properties under different conditions. The second part of this new study will focus on a new optical property model of wetted surfaces, incorporating both the dry sample measurements and existing water properties (Hale and Querry, 1973) to derive a fully analytical hybrid surface property model for input to ShipIR. Since the optical properties of water change markedly below 2.5μm, and the wetted samples can only be measured below this wavelength at zero incidence (with the sample oriented horizontally), we need at least one of the 3 samples to have a moderate (LSA) hemispherical reflectivity in this region to test and validate the new hybrid model. The current and modified (hybrid) versions of the ShipIR wetted surface reflectance model will be compared against the optical properties measured by Surface Optics Corporation.
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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".