Interaction Between Bacteria, Nannobacteria, and Mineral Precipitation in Hot Springs of Central Italy
Bibliographic record
Abstract
A complex of inorganic and organic factors controls precipitation of carbonates in hot springs of Lazio, central Italy. A plot of data from this area shows that the main /norganic controls are temperature and Mg/Ca ratio of the spring waters. Virtually all springs with waters hotter than 400C precipitate aragonite, and cooler ones form calcite. Furthermore, even cold-water springs precipitate aragonite if the Mg/Ca ratio exceeds 1:1, except in two cases. To what extent is the precipitation of travertine inorganic vs. biochemical? Surely, conditions in diverse localities can vary between both end-points, but Le Zitelle springs, at the north flank of the caldera of Viterbo, provide a biochemical extreme. Waters are hot (600C), with Mg/Ca of .2, and are highly sulfurous. Carbonate precipitation rates can exceed 2 mm/day. /Vonetched samples of carbonate crusts, only minutes to a few hours old, exhibit aragonite, calcite, and 1- to 5- (im euhedral rhombs of probable dolomite. Aragonite forms spherical "pincushions" of radial needles, each needle tipped with a nannobacterial body of the same diameter as the needles, 0.1 to 0.4 jim. Each nannobacterium precipitated its own needle, and was propelled outward by needle growth. As little or no later "fattening" of the needle occurred, inorganic precipitation must have been insignificant here. fVonetched calcite crystals are composed of 0.05 (xm nannobacterial spheres that were incorporated into each layer of the crystal as it grew. No evidence of bacteria was found on the ?dolomite rhomb surfaces. Ironically, aragonite, calcite, and euhedral ?dolomite rhombs all grew within minutes to an hour of each other in the same solution under the same conditions, savaging all the rules exposed at the beginning they remain a baffling problem unresolved by chemistry, physics, or microbiology.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".