Field campaign data analysis in support of the future FORUM and CAIRT ESA missions
Bibliographic record
Abstract
This work aims at analysing the measurements obtained from two instruments, \nFIRMOS and GLORIA, during their employment in the HEMERA ballon cam- \npaign in Timmins (Canada) in the period 23-24 August 2022. \nThe first part of this work deals with the geolocation of the GLORIA data. \nAfter that the focus is posed on the scene classification which is obtained by the \napplication of the CIC (Cloud Identification and Classification) algorithm. CIC \nis a supervised machine learning code based on PCA \nPrincipal Component Analyses that is able to perform cloud identification and \nclassification from high spectral resolution data at infrared wavelengths. The CIC \ncode has been recently tested on the identification of cloudy scenes. The classification method is based on a \ndistributional approach of similarity index computed from the element of the \ndataset with respect to the training set made available to the algorithm. Two \ndifferent versions of the classificator are tested. For \nthe first time, the CIC algorithm is applied to upwelling radiance fields in this \nconfiguration. Another novelty is the CIC application to high spatial resolution \ndata from GLORIA++ to perform a soil classification. The exercise is an important \ntest within the studies aiming at improving the initial guess of the geophysical \nretrieval of future satellite sounders. \nThe last section of this work describes the application of FARM algorithm, an \noptimal estimation based retrieval code relying on an innovative forward model \nσ − F ORU M . σ − F ORU M is a modify version of the σ − IASI code which is a \nmonochromatic fast code for calculating synthetic radiances in the 10-2760 cm−1 \nspectral range. The goal of this analyses is to retrieve atmospheric and surface \nparameters such as: surface temperature and emissivity, and temperature profiles \nand H2O vertical profiles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".