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Record W2916118585 · doi:10.1515/corrrev-2018-0041

Sulfate-reducing bacteria-assisted cracking

2019· article· en· W2916118585 on OpenAlexfundno aff
Tangqing Wu, Cheng Sun, Maocheng Yan, Jin Xu, Fucheng Yin

Bibliographic record

VenueCorrosion Reviews · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaUniversity of Calgary
KeywordsCrackingSulfate-reducing bacteriaMaterials scienceSulfateForensic engineeringBiochemical engineeringMetallurgyComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Field and laboratory studies have verified that sulfate-reducing bacteria (SRB) can assist in cracking, but there is no comprehensive review in literature related to this research. In this paper, a mini-review was done giving the available information on SRB-assisted cracking, including actual cases, laboratory investigations, thermodynamic interpretation, cracking mechanisms, and affecting factors. Furthermore, the existing problems were regularly extracted, and the possible development tendency prospected.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0650.047

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 head0.022
GPT teacher head0.265
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations12
Published2019
Admission routes1
Has abstractyes

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