Formation of Dolomite in Recent Island-Arc Sediments Due to Gas-Seawater-Sediment Interaction
Bibliographic record
Abstract
Abstract Dolomite occurs as a primary precipitate that cements Recent volcaniclastic sands in the shallow-marine environment of Luise Harbor, Lihir Island, Papua New Guinea. Field observations and geochemical data suggest that dolomite precipitates through complex mineral reactions in response to discharge of gas (> 90% CO2) and heated seawater from hydrothermal vents in the seafloor. Gas discharge causes the formation of shallow circulation cells, and cold seawater is drawn into the sediment, where it is heated to approximately 100°C. Reaction between hydrothermal CO2 (g), primary sediment grains, and heated seawater causes the precipitation of primary dolomite. Once temperatures are above 70°C the Mg/Ca ratio in seawater increases because CaSO4 precipitates, while at the same time the dissolution of iron oxide minerals increases the pH; both reactions favor dolomite precipitation. Ferrous iron reacts with H2S to form co-precipitated Fe-sulfide minerals. The dolomite is nearly stoichiometric and occurs as zoned inclusion-rich and inclusion-poor spherulitic cements, many of which are present as coalesced composite spherules. Oxygen isotope values for dolomite of -8.8 to -10.2‰ VPDB constrain the temperature of precipitation to be between about 77 to 110°C. Strontium isotope ratios for dolomite (average 0.70886) are close to those of modern seawater; their slightly less radiogenic compositions implicate minor addition of magmatic strontium. Hydrothermal dolomite may develop in shallow, near-surface sediments in active tectonic settings.
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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 | 0.000 | 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) | 0.001 | 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".