Microbes and mineral precipitation, Miette Hot Springs, Jasper National Park, Alberta, Canada
Bibliographic record
Abstract
At Miette Hot Springs, SO42/H2S-, Ca2+-, Sr2+-, and CO32-rich waters with a mean temperature of 51.2 °C are ejected from three spring vents and several minor seeps near the floor of Sulphur Creek valley. Runoff channels from the springs are colonized by cyanobacteria (Oscillatoria, Phormidium, Gloeocapsa, Synechococcus, Xenococcus) that grow in resistant mats and as loose filaments within 0.5 m of the spring vents, diatom assemblages (Cymbella, Mastagloia, Brachysira, Sellaphora, Rhopalodia, Nitzschia, Navicula, Pinnularia) that dominate the flow paths 0.52.0 m from the vents, and microbial mats with cyanobacteria and diatoms in the distal flow paths. Sulphate-reducing bacteria and green algae are also present. Gypsum, elemental sulphur, and lesser quantities of calcite and strontianite precipitate from the spring waters. Microbial populations influence accumulation of mineral precipitates by (i) forming mats close to the spring vents on which crystals grow, (ii) forming mats alongside the flow paths that trap and bind precipitates, and (iii) providing loose filaments to which microscopic gypsum crystals adhere. The microbes also influence crystal habit by (i) creating pores on the surfaces of gypsum crystals where smaller crystal precipitates form, and (ii) producing intercellular mucus in microbial mats, where suspended crystals can grow in all directions to produce polyterminal calcite crystals. Diatoms also mediate corrosion of the faces of calcite and gypsum crystals. Enriched δ13Cinorganicsignatures in the precipitates associated with microbial communities indicate that photosynthesis may promote precipitation of calcite and strontianite.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".