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Record W2807048866 · doi:10.1515/corrrev-2017-0094

Performance of green corrosion inhibitors from biomass in acidic media

2018· article· en· W2807048866 on OpenAlex
Andrea Marciales, Tesfaalem Haile, Behzad Ahvazi, Tri-Dung Ngo, John Wolodko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCorrosion Reviews · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsDevon Energy (Canada)University of Alberta
Fundersnot available
KeywordsCorrosionBiomass (ecology)Environmentally friendlyMaterials scienceRenewable resourceBiochemical engineeringRenewable energyNanotechnologyMetallurgyBiologyEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract There has been a strong interest worldwide in developing suitable technologies that can derive chemicals and materials from renewable biomass in a number of applications, including corrosion inhibitors. In spite of the efficacy of conventional inhibitors in corrosion control, current corrosion inhibitors exhibit toxicity and/or are non-biodegradable. Therefore, industry efforts are leading to the development of non-toxic and environmentally friendly “green” corrosion inhibitors from renewable resources. Extensive studies of different bio-based inhibitors show that a vast number of phytochemicals can be used as efficient corrosion inhibitors. This paper provides a comprehensive review of the corrosion inhibition properties of plant-derived chemicals classified under the parameters provided by botanical chemistry.

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.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.036
GPT teacher head0.279
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