MétaCan
Menu
Back to cohort
Record W4220952571 · doi:10.3390/app12073426

Partial Replacement of Petroleum Coke with Modified Biocoke during Production of Anodes Used in the Aluminum Industry: Effect of Additive Type

2022· article· en· W4220952571 on OpenAlexafffund
Belkacem Amara, Duygu Kocaefe, Yaşar Kocaefe, Dipankar Bhattacharyay, Jules Côté, André Gilbert

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsBoisaco (Canada)Aluminerie Alouette (Canada)Université du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologiesUniversité du Québec à Chicoutimi
KeywordsPetroleum cokeCokeAnodeMaterials scienceEnvironmental scienceGreenhouse gasWaste managementPulp and paper industryMetallurgyChemistryEngineeringElectrode

Abstract

fetched live from OpenAlex

In order to reduce greenhouse gas (GHG) emissions, biocoke modified with different additives was used to replace part of the petroleum coke. Previously, a number of researchers attempted to manufacture anodes using biocoke. However, the majority of these efforts were unsuccessful because the quality of the anodes deteriorated with this replacement. The deterioration was due to the weak interactions between the pitch and biocoke compared to those between the pitch and petroleum coke. In this study, a chemical modification of biocoke was carried out using three additives (i.e., A(1), A(2), and A(3)) with the aim of improving biocoke–pitch interactions to prevent the deterioration of anode quality. The results of this study showed that biocoke–pitch interactions improved when the biocoke was modified with A(1) and A(3). The anodes containing biocoke modified with these two additives had properties similar to those of the standard anode (i.e., without biocoke). The utilization of additive A(2) did not show the same trend.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueApplied SciencesSame topicFiber-reinforced polymer compositesFrench-language works237,207