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Record W2009189937 · doi:10.1002/srin.201200339

Adiabatic Carbon Rate of Alternative Ironmaking Processes to Produce Hot Metal

2013· article· en· W2009189937 on OpenAlex

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

Venuesteel research international · 2013
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsYardstickSmeltingCarbon fibersAdiabatic processReduction (mathematics)Work (physics)Environmental scienceCoalProcess (computing)Waste managementMaterials scienceProcess engineeringMetallurgyThermodynamicsEngineeringMathematicsComputer scienceComposite material

Abstract

fetched live from OpenAlex

Abstract From economic viability and sustainable development of view, the future alternative ironmaking processes, in general, may be classified into two representative routes, “Pre‐reduction‐Smelting reduction” process and “Direct reduction‐Melting” process. In the present work, aimed for hot metal production, the carbon rate (in kg tHM −1 ) is considered as the yardstick for the measurement of the “goodness” of these two processes, because it reflects the consumption of coal and energy, and the amount of carbon dioxide eventually to be emitted to atmosphere. By the thermodynamic calculation, under the idealized conditions, the total adiabatic carbon rate along “Direct reduction‐Melting” process is approximately 145 kg tHM −1 lower than that along “Pre‐reduction‐Smelting reduction” process. Therefore, direct reduction would likely lead to an alternative process with much lower carbon rate, and it should be reasonable and successfully developed.

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)0.001
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 score0.131
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.055
GPT teacher head0.340
Teacher spread0.284 · 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