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Record W3093743104

Bacterial and Thermochemical Sulfate Reduction in Diagenetic Settings - Old and New Insights

2006· article· en· W3093743104 on OpenAlexaff
Hans G. Machel

Bibliographic record

VenueAGU Fall Meeting Abstracts · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDiagenesisSulfateDissolutionAnhydriteGeologyGeochemistrySulfurMineralogyEnvironmental chemistryChemistryPaleontologyGypsumOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

The association of dissolved sulfate and hydrocarbons is thermodynamically unstable in virtually all diagenetic environments. Hence, redox-reactions occur, whereby sulfate is reduced by hydrocarbons either bacterially (bacterial sulfate reductiona BSR) or inorganically (thermochemical sulphate reductiona TSR). Their geologically and economically significant products are similar. Based on empirical evidence, BSR and TSR occur in two mutually exclusive thermal regimes, i.e. low-temperature and hightemperature diagenetic environments, respectively. BSR is common in diagenetic settings from 0 up to about 60‐808C. Above this temperature range, almost all sulfate-reducing microbes cease to metabolize. Those few types of hyperthermophilic microbes that can form H2S at higher temperatures appear to be very rare and do not normally occur and/or metabolize in geologic settings that are otherwise conducive to BSR. TSR appears to be common in geologic settings with temperatures of about 100‐1408C, but in some settings temperatures of 160‐1808C appear to be necessary. TSR does not have a sharply defined, generally valid minimum temperature because the onset and rate of TSR are governed by several factors that vary from place to place, i.e. the composition of the available organic reactants, kinetic inhibitors and/or catalysts, anhydrite dissolution rates, wettability, as well as migration and diffusion rates of the major reactants toward one another. BSR is geologically instantaneous in most geologic settings. Rates of TSR are much lower, but still geologically significant. TSR may form sour gas reservoirs and/or MVT deposits in several tens of thousands to a few million years in the temperature range of 100‐1408C. BSR and TSR may be exothermic or endothermic, depending mainly on the presence or absence of specific organic reactants. However, if the reactions are exothermic, the amount of heat liberated is very small, and this heat usually dissipates quickly. Hence, heat anomalies found in association with TSR settings are normally not generated by TSR. The main organic reactants for BSR are organic acids and other products of aerobic or fermentative biodegradation. The main organic reactants for TSR are branched and n-alkanes, followed by cyclic and mono-aromatic species, in the gasoline range. Sulfate is derived almost invariably from the dissolution of gypsum and/or anhydrite, which may be primary or secondary deposits at or near the redox-reaction site(s). The products of BSR and TSR are similar, but their relative amounts vary widely and are determined by a number of locally variable factors, including availability of reactants, formation water chemistry, and wettability. The primary inorganic reaction products in both thermal regimes are H2S(HS 2 ) and HCO3 (CO2). The presence of alkali earth metals often results in the formation of carbonates, particularly calcite and dolomite. Other carbonates, i.e. ankerite, siderite, witherite, strontianite, may form if the respective metal cations are available. Iron sulfides, galena, and sphalerite form as by-products of hydrogen sulfide generation, if the respective transition or base metals are present or transported to a BSR/TSR reaction site. Elemental sulfur may accumulate as a volumetrically significant

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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 designObservational
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
Published2006
Admission routes1
Has abstractyes

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