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Record W1484002102 · doi:10.5006/c2000-00055

Inhibitor Selection for Internal Corrosion Control of Pipelines: Comparison of Rates of General Corrosion and Pitting Corrosion under Gassy-Oil Pipeline Conditions in the Laboratory and in the Field

2000· article· en· W1484002102 on OpenAlexaff
Sankara Papavinasam, R. Winston Revie, Michael Attard, Alebachew Demoz, J. C. Donini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCorrosionPipeline transportPitting corrosionPipeline (software)MetallurgyMaterials sciencePetroleum engineeringEnvironmental scienceEngineeringEnvironmental engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Field experiments were carried out in a gassy-oil field using two continuous inhibitors, each at four concentrations, 0, 50, 100 and 200 ppm, and two batch inhibitors, each at two concentrations, 0 and 2000 ppm. Laboratory experiments were carried out using 12 different methodologies with the same inhibitors at the same concentrations as used in the field. By comparing the general and pitting corrosion rates in field and laboratory experiments at the same inhibitor concentration, a ranking of laboratory methodologies has been developed. The approaches used to calculate the ranking of the laboratory methodologies were: Comparison of the logarithm of the ratio of the general corrosion rate in the laboratory to that in the field;Comparison of the logarithm of the ratio of the pitting corrosion rate in the laboratory to that in the field; andComparison of the percent inhibition (calculated from both general corrosion rate and pitting corrosion rate) in the laboratory and in the field.

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 score0.032
Threshold uncertainty score0.395

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 head0.018
GPT teacher head0.315
Teacher spread0.297 · 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

Citations9
Published2000
Admission routes1
Has abstractyes

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