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Record W2765194502 · doi:10.5151/2594-357x-30440

VALUE IN USE OF PASEK DUNITE IMPROVING THE RETURN SINTER FINES

2017· article· pt· W2765194502 on OpenAlexaff
Aitor Elorriaga Fernandez de Arroyabe, Javier Martinez Rubio, Pamela Diaz Garcia, Alba Fernández Fernández

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldArts and Humanities
TopicAncient and Medieval Archaeology Studies
Canadian institutionsASTER
Fundersnot available
KeywordsValue (mathematics)MetallurgyMaterials scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

The purpose of Sinter plants is to heat iron ore fines along with fluxes and coke fines or coal, to produce a semi-molten mass that solidifies into porous pieces of sinter with the needed size and strength. Fluxes used in the sintering process need to have an appropriate size distribution and TI to avoid generation of fines, which could reduce the productivity of the Sinter. Besides, a homogeneous chemical composition and high absorption characteristics are necessary to control the basicity and to combine the flux with P, S, Si, etc, respectively. PASEK Dunite is an ultramaphic rock exploited in the north of Spain with a basic chemical classification, being olivine and serpentine its principal minerals. It is a very homogeneous and hard stone and its main characteristics are: high hot and cold resistance to mechanical stresses, softening and melting points of a flux not of a refractory material. In addition, due to its mechanical and chemical properties, less fines are generated in the sintering process and during the handling and transportation to the blast furnace, improving the productivity of these processes. In this way, the use of PASEK Dunite allows increasing the homogeneity structure of sinter, reducing the percentage of return fines and improving the Sinter productivity.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.993

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.085
GPT teacher head0.269
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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