Ice nucleation by surrogates of Martian mineral dust: What can we learn about Mars without leaving Earth?
Bibliographic record
Abstract
Abstract Water and carbon dioxide ice clouds have been observed in the Martian atmosphere where they are dynamic parts of that planet's water and carbon cycles. Many Martian atmospheric models struggle to correctly predict clouds and, with insufficient data, some use untested simplifications that cloud formation occurs exactly at the saturation point of the condensed phase or at the same conditions as terrestrial cirrus clouds. To address the lack of data, we have utilized an 84 m3 cloud chamber built for studies of high altitude cirrus and polar stratospheric ice clouds in the Earth's atmosphere and adapted to Martian conditions. Using this chamber, we have been able to produce water ice clouds from aerosol in an inert and low pressure atmosphere mimicking that of Mars. At temperatures between 189 and 215 K, we investigated cloud formation by mineral dust particulates of a similar composition and size to those found on Mars. We show that these surrogate materials nucleate effectively at the higher temperatures, with minor temperature dependence at saturations ratios with respect to the ice phase of ~1.1, similar to what has been found for terrestrial cirrus. At the lower end of the temperature range, this saturation rises to ~1.9, a result consistent with previous studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".