MATADOR 2002: A pilot field experiment on convective plumes and dust devils
Bibliographic record
Abstract
Recent research suggests that mineral dust plays an important role in terrestrial weather and climate, not only by altering the atmospheric radiation budget, but also by affecting cloud microphysics and optical properties. In addition, dust transport and related Aeolian processes have been substantially modifying the surface of Mars. Dusty convective plumes and dust devils are frequently observed in terrestrial deserts and are ubiquitous features of the Martian landscape. There is evidence that they are important sources of atmospheric dust on both planets. Many studies have shown that on a small scale, dust sourcing is sensitive to a large number of factors, such as soil cover, physical characteristics, composition, topography, and weather. We have been doing comparative studies of dust events on Earth and Mars in order to shed light on important physical processes of the weather and climate of both planets. Our 2002 field campaign showed that terrestrial dust devils produce heat and dust fluxes two and five orders of magnitude larger than their background values. It also showed that charge separation within terrestrial dust devils produces strong electric fields that might play a significant role in dust sourcing. Since Martian dust devils and dust storms are stronger and larger than terrestrial events, they probably produce even stronger fluxes and electric fields.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".