Caractérisation de l'émissivité des surfaces terrestres à partir de données multispectrales en infrarouge médian et thermique
Bibliographic record
Abstract
Evaluating the potential of middle wave and long wave infrared emissivity for land surface characterization is the challenge of many researches. It remains a topical research subject with the arrival of new remote sensing products giving spectral emissivity images with a large spatial cover. First, we propose a sensitivity analysis of the Temperature Emissivity Separation algorithm (TES) developed for the ASTER sensor and that we adapted for ground based radiometric measurements. The empirical relationship between minimum emissivity and spectral emissivity contrast, on which the TES is based, was validated for 3 and 5 band radiometers in the thermal infrared, with a large dataset. According to our digital simulations, it is possible to derive emissivity and temperature with an accuracy of 0,03 and 1.2K respectively. Secondly, emissivities provided by ASTER (TES algorithm) and MODIS (based on 2 different algorithms,"Classification based emissivity method" and"Day/Night land surface temperature algorithm") were compared for images over northern Canadian regions.--Résumé abrégé par UMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".