Classification of cloud scenes by Argus spectral data
Bibliographic record
Abstract
The mini-spectrometer Argus 1000 being in space, continuously monitors the sources and sinks of the trace gases. This paper presents a methodology of classification of cloud scene by Argus Spectral Data (CCSArSD) by applying radiance enhancement (RE) technique within 900-1700 nm of wavelength bands at infrared sounder along with GENSPECT line by line radiative transfer code for different weeks per passes. Argus was launched on aboard CanX-2 micro-satellite on 28th April 2008 as part of a technology demonstration mission. The algorithm describes a method to detect the cloudy or non-cloudy scenes. We have collected more than 300 weeks per passes with each have more than 200 spectra. The REi within the selected wavelength bands of Argus, provides a promising results to classify the cloud scene. We moreover worked on the shortwave upwelling radiative flux (W/m2) to improve the CCSArSD model, which needs further study to jump up to higher rank.
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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 | 0.003 | 0.001 |
| 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) | 0.001 | 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".