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Record W2913686478 · doi:10.1002/ceat.201800205

Colorimetric Methods for Determining Fe, V, and Ni Contents in Coke and Anodes

2019· article· en· W2913686478 on OpenAlex
Hang Sun, Duygu Kocaefe, Dipankar Bhattacharyay, Yaşar Kocaefe, Jules Côté, Patrick Coulombe

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemical Engineering & Technology · 2019
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Chicoutimi
KeywordsCokeAnodePetroleum cokeCarbon fibersRaw materialImpurityReagentMetallurgyMaterials scienceCoal tarCoalMetalChemistryElectrodeComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Prebaked carbon anodes are used in the electrolytic production of aluminum. They are made of petroleum coke, butts, recycled anodes, and coal tar pitch. The anode quality, which depends on the raw material quality and the production conditions, has an important impact on the cell performance. Metallic impurities (V, Ni, and Fe) found in cokes and anodes increase the carbon consumption by catalyzing the air and CO 2 reactivities. In turn, this increases the production cost, energy consumption, and the emission of greenhouse gases. The current methods for detecting the metallic impurities in carbon are time consuming and require intensive sample preparation, skilled personnel, and costly reagents. In this work, simple, rapid, and effective tools were developed using colorimetric methods.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.282
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.017
GPT teacher head0.297
Teacher spread0.279 · 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