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Record W4239192266 · doi:10.1002/9781119274780.ch49

Rio Tinto AP44 Cell Technology Development at Alma Smelter

2016· other· en· W4239192266 on OpenAlexaff
Pascal Thibeault, Hervé Mézin, Olivier Martin

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsRio Tinto (Canada)
FundersForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsMilestoneSmeltingTechnology developmentAluminium smeltingEngineeringGeographyMetallurgyArchaeologyManufacturing engineeringMaterials science

Abstract

fetched live from OpenAlex

The AP30 platform reached an important milestone at the Alma Smelter. This latter is the first to operate above 400 kA. Following this success, Rio Tinto Aluminium group launched the AP44 cell development to offer a technology capable of operating above 440 kA. This represents fifty percent more than the original AP30 cell. The technology is expected to deliver world-class performance such as metal production of 3274 kg/day/cell and an energy consumption of approximately 13.23 kWh/kg.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.011

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2016
Admission routes1
Has abstractyes

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