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Record W4375853788 · doi:10.1139/cjm-2023-0015

Distribution of corrosive microbial communities in rust layers of steel immersed in seawater

2023· article· en· W4375853788 on OpenAlexvenueno aff
Q Ren, Yimeng Zhang, Jizhou Duan, Wangqiang Li, Xucheng Dong, Yingying Pei, Ruiyong Zhang, Qingjun Zhu

Bibliographic record

VenueCanadian Journal of Microbiology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsSeawaterDesulfovibrioSulfate-reducing bacteriaBacteriaSulfateEnvironmental chemistryCarbon fibersTotal organic carbonCarbon steelChemistryBiologyMetallurgyEcologyMaterials science

Abstract

fetched live from OpenAlex

In this paper, high-throughput sequencing technology was adopted to visualize the microbial communities on the surfaces of two types of carbon steel immersed in Sea Area Ⅰ. The results showed that different microbial communities were formed on different carbon steel surfaces, in which the genus with the highest abundance on the surface of Q235 was Escherichia– Shigella, while anaerobic Desulfovibrio on the surface of 921a was the most abundant, and the dominant genus varied with the depth of the rust layer. In addition, the distribution of sulfate-reducing bacteria (SRB) on the surface of Q235 submerged in Sea Area Ⅱ was compared with the sulfate-reducing bacteria's distribution in Sea Area Ⅰ, using the environmental factors correlation analysis. The results showed that the concentrations of Ca 2+ , Na + , K + , Mg 2+ , and Al 3+ were positively correlated with the distribution of SRB, while the concentrations of Cu 2+ , Zn 2+ , SO 4 2− , Cl − , NO 3 − , and organic carbon were negatively correlated with it. Furthermore, there was a highly significant correlation between each geochemical factor and Desulfotomaculum ( p < 0.01).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.022
GPT teacher head0.239
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2023
Admission routes1
Has abstractyes

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