Overview of the magnetic properties experiments on the Mars Exploration Rovers
Bibliographic record
Abstract
The Mars Exploration Rovers have accumulated airborne dust on different types of permanent magnets. Images of these magnets document the dynamics of dust capture and removal over time. The strongly magnetic subset of airborne dust appears dark brown to black in Panoramic Camera (Pancam) images, while the weakly magnetic one is bright red. Images returned by the Microscopic Imager reveal the formation of magnetic chains diagnostic of magnetite‐rich grains with substantial magnetization (>8 Am2 kg−1). On the basis of Mössbauer spectra the dust contains magnetite, olivine, pyroxene, and nanophase oxides in varying proportions, depending on wind regime and landing site. The dust contains a larger amount of ferric iron (Fe3+/Fetot ∼ 0.6) than rocks in the Gusev plains (∼0.1–0.2) or average Gusev soil (∼0.3). Alpha Particle X‐Ray Spectrometer data of the dust show that some of the iron in magnetite is substituted by titanium and chromium. The good correlation of the amount of calcium and sulfur in the dust may be caused by the presence of a calcium sulfate related phase. The overall mineralogical composition points to a basaltic origin of the airborne dust, although some alteration has taken place as indicated by the large degree of oxidation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".