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Record W3039685176 · doi:10.1002/maco.202011770

Modeling microbial sulfate reduction and the consequences for corrosion of copper canisters

2020· article· en· W3039685176 on OpenAlexaff
Fraser King, Miroslav Kolàř, I. Puigdomènech, Petteri Pitkänen, Christina Lilja

Bibliographic record

VenueMaterials and Corrosion · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsMackinawiteCorrosionSulfateDissolutionSulfideSideriteCopperOrganic matterGypsumChemistrySulfate-reducing bacteriaEnvironmental chemistryEnvironmental scienceMaterials scienceMetallurgyPyriteMineralogy

Abstract

fetched live from OpenAlex

Abstract The copper sulfide model (CSM) is a one‐dimensional reactive transport code for predicting the evolution of the corrosion behavior of a copper canister in a deep geological repository. Here, the CSM has been extended to simulate the microbial reduction of sulfate in the repository and the consequences for corrosion of the canister. Organotrophic and chemotrophic sulfate reduction are represented by Monod kinetics, along with the dissolution of solid organic matter and gypsum as sources of nutrient and an electron acceptor, respectively. Siderite dissolution in the buffer and tunnel backfill materials acts as a source of Fe(II), which can then precipitate the microbially produced sulfide as mackinawite. Results are presented for a simulation representing the expected evolution of the corrosion behavior and repository environment and for a series of sensitivity analyses designed to identify the most important processes in the overall reaction scheme.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.247
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations24
Published2020
Admission routes1
Has abstractyes

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