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Cold briquetting of sponge iron (CBSI): parameters and effectiveness

2011· article· en· W1998494346 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

VenueIronmaking & Steelmaking Processes Products and Applications · 2011
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDirect reduced ironFerrousMetallurgyAnthraciteSpongeCast ironMaterials scienceWaste managementEngineeringCoal

Abstract

fetched live from OpenAlex

The oxidation of sponge iron products and its destructive effects on iron content as well as other properties have been considered by ferrous technologists for a long time. In this investigation, a unique new low temperature method of preventing and/or retarding the oxidation of direct reduced iron is introduced. Experimental results from laboratory and pilot plant tests show the success of the method which has been named cold briquetting of sponge iron. The optimum condition for briquetting is achieved at 80 kN (Lcm)−1, 6% binder, 10% sponge iron fines and 4% anthracite. While hot briquetted iron has become a successful method for the new generation of iron reduction plants, cold briquetting of sponge iron could become a suitable technique to protect direct reduced iron products against oxidation for traditional iron reduction units.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
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
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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