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Record W4249385755 · doi:10.1002/9781119274780.ch125

Aluminium Casting Furnace Energy Efficiency: Recent Improvements in Rio Tinto Aluminium Casthouses

2016· other· en· W4249385755 on OpenAlex
Vincent Goutière, Martin Fortier, Claude Dupuis

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

Venuenot available
Typeother
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsRio Tinto (Canada)
Fundersnot available
KeywordsAluminiumCastingMetallurgyEnergy consumptionReduction (mathematics)Efficient energy useNatural gasEnvironmental scienceMaterials scienceEngineeringWaste managementMathematics

Abstract

fetched live from OpenAlex

In 2005, Rio Tinto Aluminium started an ambitious program to reduce casting furnace energy consumption in its casthouses. A major reduction step was reached in 2008 by implementing this energy efficiency program in all casthouses, focusing more specifically on the reduction of natural gas consumption (m3 of gas) and specific energy consumption (MJ/t of cast metal). In order to further reduce energy consumption, a new strategy was recently developed and is described in this paper.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.230
Teacher spread0.220 · 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

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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