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Record W4391817132 · doi:10.1177/1478422x241233074

Research on the corrosion behavior of floating bead low-density cement stone/P110 steel system under different CO <sub>2</sub> pressure

2024· article· en· W4391817132 on OpenAlexaff
Shuliang Wang, Zidan Wen, Shuai Zhang, Mengjun Yao, Wanneng Lei, Mingyu Wu, Yongbing Liu, Xiaoyan Zhang, Xianguang Zeng, Shidong Wang

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2024
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsCorrosionMaterials scienceCementBeadMetallurgyAtmospheric pressureComposite materialGeology

Abstract

fetched live from OpenAlex

This study investigates the corrosion state of the floating bead low-density cement stone/P110 steel system in simulated CO 2 -saturated formation water under high and atmospheric CO 2 pressure. Results show that the resistance of the cement stone reaches a maximum value at 28 days and then decreases. The corrosion rate of P110 steel under high pressure is greater than that under atmospheric pressure. There is a certain gap and accumulation of corrosion products between the cement stone and the steel. Additionally, the maximum gap size and corrosion product thickness reach 27.5 and 67.69 μm, respectively. The composition of the corrosion products on the steel surface is mainly FeCO 3 , and the presence of the iron oxides are caused by oxidation. The corrosion products of the cement are also confirmed to be CaCO 3 and a small amount of SiO 2 .

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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