10.1016/0967-0653(96)86865-7
Bibliographic record
Abstract
Backscattering by non-spherical ice particles at 94 GHz (3.2 mm wavelength) has been examined for hexagonal cylinders with aspect ratios ranging from 0.2 to 10, i.e. from plate-like to needle-like particles, as well as for combinations of hexagonal columns. Calculations have been performed using the Discrete Dipole Approximation (DDA). The results are of particular interest for the recently available 94 GHz cloud radar systems. In order to estimate the required number of dipoles to model a given particle, two empirical criteria have been formulated, which take into account the size and the non-sphericity of the particle. The origins of errors in the DDA method have been investigated. The effects of neglecting higher orders of multipole terms are much smaller than the errors due to the approximation of the particle shape by arranging dipoles on a cubic array. The backscattering properties of horizontally oriented ice particles have been calculated. Both the backscattered radiance and the depolarization ratio strongly depend on the particle size and aspect ratio. In general, the radiance increases with increasing particle size. However, resonance effects cause local minima in the backscattered radiances. The locations of these minima in terms of size and aspect ratio provide a relationship between these two parameters. Depolarization is strongest for needle-like particles and decreases with decreasing aspect ratio. In particular, the maximum depolarization is strongly related to the aspect ratio. Thus, observing local maxima of depolarization may provide an identification of the particle shape, which in turn may be converted to the size. The backscattering properties of combinations of hexagonal columns are similar to those for a single hexagonal column with a modified aspect ratio. It seems to be possible to treat these complex particles as simple hexagonal columns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.986 | 0.991 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".