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Record W1502171873 · doi:10.5006/c2005-05627

Inhibitor Developments Providing Mitigating Benefits against Pitting Corrosion to Carbon Steel Constructed Assets Used to Process Wet Sulfur Contaminated Sour Gas Production

2005· article· en· W1502171873 on OpenAlexaffabout
Michael R. Gregg, J. A. Lerbscher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsBaker Hughes (Canada)
Fundersnot available
KeywordsSour gasSulfurMetallurgyCarbon steelCorrosionEnvironmental sciencePitting corrosionWaste managementMaterials scienceEngineeringNatural gas

Abstract

fetched live from OpenAlex

Abstract The unmitigated general and pitting corrosion rates were measured for carbon steel coupon specimens subjected to a series of sulfur (S8) spiked laboratory test conditions. Tests simulate sour (containing hydrogen sulphide gas, H2S) gas production environments currently challenging inhibitor programs in Western Canada to protect carbon steel constructed production assets from sulfur catalyzed pitting corrosion attack. The mitigated general and pitting corrosion rates were also measured for coupon specimens subjected to sulfur spiked laboratory test conditions simulating the pipeline production environments. This study evaluated effectiveness of continuous injection type corrosion inhibitors for providing mitigating benefits against pitting attack under conditions, with and without the aide of a batch inhibitor film pre-applied to the carbon steel specimen. The mitigated study concluded with an evaluation of inhibitors for providing mitigating benefits against corrosion under test conditions simulating down hole production environments, likewise challenging inhibitors to protect carbon steel constructed tubing and production assets operated throughout Western Canada from sulfur catalyzed pitting corrosion attack.

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 score0.010
Threshold uncertainty score0.021

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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

Citations8
Published2005
Admission routes2
Has abstractyes

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