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Record W4416015144 · doi:10.1002/9781394229185.ch14

Geochemical and Geophysical Controls on Hydrothermal Fluxes on Habitable Worlds

2025· other· en· W4416015144 on OpenAlexaff
Benjamin M. Tutolo, Nicholas J. Tosca

Bibliographic record

VenueGeophysical monograph · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHydrothermal circulationHydrothermal ventHabitabilitySeafloor spreadingEarly EarthEarth (classical element)AbiogenesisAtmosphere (unit)

Abstract

fetched live from OpenAlex

Hydrothermal systems strongly contribute to planetary habitability and must be considered in the search for life in the universe. Studies of hydrothermal systems on Earth can provide insight into the role these systems play in dictating planetary habitability. Yet, because they are intimately connected to plate tectonics and the history of Earth's ocean–atmosphere system, most hydrothermal systems on Earth probably differ from those that feature on other rocky planetary bodies. We provide two comparative studies to show how hydrothermal processes in our modern oceans likely differ from their ancient analogues and those on prebiotic planetary surfaces. We first outline the role of elevated seawater sulfate in controlling the chemistry and style of modern high-temperature hydrothermal vents and discuss how oxygenation of the atmosphere and oceans has thus affected our view of seafloor hydrothermal systems. We then examine how the evolution of silicifying organisms yields different minerals and fluxes in serpentinizing systems on the modern ocean floor relative to their ancient counterparts. We conclude by outlining the potential for adapting existing geochemical tools to study the contributions of hydrothermal systems to the habitability of planetary bodies, including exoplanets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.002

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.

Opus teacher head0.005
GPT teacher head0.227
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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