Analysis of air-soil temperature differences at five locations for applications in passive standoff chemical detection
Bibliographic record
Abstract
For a passive spectral sensor, the temperature difference (DT) that exists between a chemical cloud and the background scene is of prime importance because it is linked to the radiative contrast of the target. The larger DT, the better the radiative contrast and the more accurate is the detection and identification, of the cloud. This paper establishes statistics on realistic air-soil DT to be used to estimate the detection performance of passive spectral sensors in a variety of scenarios and environments. To this end, an analysis of the air-soil DT is presented for five locations around the world. The results of the analysis indicate that the statistics of the air-soil absolute DT are similar from one location to another. The average statistics over the five locations show a mean absolute air-soil DT of 3.5 °C and a median of 2.8 °C. An absolute air-soil DT of less than one degree Celsius occurs less than 14% of the time on the average. This suggests that, on average, air-soil temperature contrasts should yield good detection probabilities 86% of the time.
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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 | 0.001 |
| 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".