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Record W2335016541 · doi:10.1515/htmp-2014-0016

Phosphate Capacities of CaF <sub>2</sub> -MgO and CaF <sub>2</sub> -CaO-MgO Slags

2014· article· en· W2335016541 on OpenAlexaff
Fatemeh Akbari, C.A. Pickles

Bibliographic record

VenueHigh Temperature Materials and Processes · 2014
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhosphateMaterials sciencePhosphorusPartition (number theory)Partition coefficientAlloyMetallurgyInorganic chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Previously published sulphide capacity data and thermodynamic arguments have been employed to calculate the phosphate capacities and the phosphorus partition ratios between a molten carbon saturated iron alloy and binary CaF 2 -MgO slags and also ternary CaF 2 -CaO-MgO slags at 1450 °C. For the CaF 2 -MgO binary system, a linear relationship was found between the phosphate and the sulphide capacities as follows: log = 1.2 log C s + 25.2. For the ternary CaF 2 -CaO-MgO system at 1450 °C, the logarithm of the calculated phosphate capacities ranged from 19.47 to 20.15. With the addition of CaO, the phosphate capacities initially increased, reached a maximum and then decreased slightly. The addition of MgO to the CaF 2 -CaO system resulted in a decrease in the phosphate capacity. The calculated phosphorus partition ratios increased slightly with increasing mole fraction of CaO in the ternary system.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.161
Teacher spread0.158 · 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 designBench or experimental
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

Citations2
Published2014
Admission routes1
Has abstractyes

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