A Brief Introduction to Potential Links and Feedbacks Between Hydrothermal Processes and Seawater Chemistry
Bibliographic record
Abstract
The chemical composition of the oceans plays a critical role in many aspects of the Earth system across all timescales, and the same is likely to be true on any habitable planet. It has long been accepted that changes in Earth's climate and atmospheric carbon dioxide concentration affect chemical fluxes from the continents to the ocean, which have commonly been considered the key drivers of changing seawater chemistry. Recent studies, however, have suggested that Earth's ocean temperature, composition, and sedimentation history are also critical in controlling chemical exchange between seawater and the underlying ocean crust through changes in hydrothermal fluxes. For example, studies have demonstrated that iron, an important micronutrient in the ocean, is transported thousands of kilometers from on-axis hydrothermal vents into the open ocean, raising questions about how hydrothermal Fe inputs might vary as ocean conditions change. Likewise, there has recently been increased interest in the role of low-temperature hydrothermal circulation (also known as seafloor weathering) in the major ion balance of seawater, and its role in the long-term carbon cycle and climate regulation both on Earth and exoplanets. Here we introduce some of the links and feedbacks between hydrothermal processes and seawater chemistry, emphasizing how they impact one another.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.141 | 0.083 |
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".