Seafloor spreading and the delivery of sulfur and metals to Earth’s oceans
Bibliographic record
Abstract
Abstract Circulating fluids within Earth’s mid-ocean ridge system cool and alter the oceanic crust, contribute distinct chemistry to the ocean, and generate economically and geologically important metal-sulfide deposits at the seafloor. Yet, we have few constraints on the characteristics of these fluids at peak subseafloor pressure and temperature conditions or how the primary variable, seafloor spreading, affects these fluids’ delivery of metals and sulfur to seawater. Here, we develop a new, robust technique for estimating the peak endowment of heat and dissolved sulfur, iron, and copper in subseafloor hydrothermal fluids and determining their fate as these superheated fluids rise to the seafloor. Calculations using this technique indicate that >20%–70% of sulfur, iron, and copper dissolved at peak subseafloor conditions are lost during upflow due to cooling and concomitant decreases in sulfide mineral solubility. The interpretation of these estimates within the geologic context of vent fields allows us to demonstrate a strong inverse relationship between seafloor spreading rate and peak pressure-temperature conditions, subseafloor heat loss, and the magnitude of subseafloor sulfide mineralization. Our results demonstrate the extent to which the secular variation of Earth’s mid-ocean ridge system over geologic time has impacted sulfide deposition rates and hydrothermal fluxes of sulfur and metals to the ocean.
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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 | 0.001 |
| 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".