VP-SEM Investigation of 3-D Surface Morphology in Cirrus-like Ice Crystals
Bibliographic record
Abstract
It has been well documented that the reflective and diffusive properties of cirrus clouds influence the radiative budget of the earth; in turn, the surface morphology of cirrus ice crystals affects those properties. This summer work aimed to quantify the surface morphology of cirrus-like ice crystals grown in a Variable Pressure Scanning Electron Microscope (VP-SEM). The implementation of this goal occurred in two stages: crystals were first grown and imaged in the VP-SEM, then a Python code was developed to reconstruct a 3-dimensional model of the surface from the images. Crystals were grown at pressures between 50 and 100 Pa and imaged at the equilibrium temperature using the backscatter electron detector (BSE).\nThe Python code, which is still under development, uses a generalization of the Blinn-Phong shading model to determine the surface normal vector of each point in the images captured from the BSE detector. From the normal vectors, a raster surface profile is reconstructed. This code has not yet produced data for cirrus ice surfaces, due to the uncertainties in its reliability for reconstructing unknown surfaces. The code will be further developed as the subject a thesis.
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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 | 0.000 | 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) | 0.002 | 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".