EFFECT OF SULFUR CONCENTRATION AND pH CONDITIONS ON AKAGANEITE FORMATION: UNDERSTANDING AKAGANEITE FORMATION CONDITIONS IN YELLOWKNIFE BAY, GALE CRATER, MARS.
Bibliographic record
Abstract
The Chemistry and Mineralogy Instrument (CHEMIN) on board the Mars Science Laboratory (MSL) Curiosity Rover identified minor amounts of akaganeite (beta-FeOOH) at Yellowknife Bay, Mars. There is also evidence for akaganeite at other localities on Mars from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). Akaganeite is an iron(III) hydroxide with a hollandite- like structure and Cl in its tunnels. Terrestrial akaganeite usually forms in Cl-rich environments under acidic, oxidizing conditions. Previous studies of akaganeite have revealed that akaganeite formation is affected by the presence of sulfate (hereafter denoted as S. The prediction of circumneutral pH coupled with the detection of S at Yellowknife Bay dictate that work is needed to determine how S and pH together affect akaganeite formation. The goal of this work is to study how changes in both S concentration and pH influence akaganeite precipitation. Akaganeite formation was investigated at S/Cl molar ratios of 0, 0.017, 0.083, 0.17 and 0.33 at pH 1.5, 2, and 4. Results are anticipated to provide combined S concentration and pH constraints on akaganeite formation in Yellowknife Bay and elsewhere on Mars. Knowledge of solution pH and S concentrations can be utilized in understanding microbial habitability potential on the Martian surface.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".