Multisensor and multiscale survey and characterization for radiometric spatial uniformity and temporal stability of Railroad Valley Playa (Nevada) test site used for optical sensor calibration
Bibliographic record
Abstract
In this study, we analyzed for the first time the potential of Getis statistics compared to the coefficient of variation for the study of the radiometric spatial uniformity and temporal stability of the Railroad Valley Playa, Nevada (RVPN) test site. We evaluated multi-sensor and multi-scale image data acquired for the RVPN, including four SPOT HRV images acquired in 1997 and 1998, five NOAA AVHRR images acquired in 1999, and one Landsat TM image acquired in 1998. The results show the potential and the importance of the synergy generated by these two methods for analyzing the radiometric spatial uniformity and temporal stability of the RVPN site. Getis statistics provide an excellent spatial analysis of the site while the coefficient of variation provides complementary information on the temporal evolution of the site.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".