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Record W3027221118 · doi:10.1016/j.jlp.2020.104184

Characterization of Ti powders mixed with TiO2 powders: Thermal and kinetic studies

2020· article· en· W3027221118 on OpenAlexaff
Fanyi Meng, Shaoqian Xue, Paul Amyotte, Chang Li, Yixiao Yuan, Qingsong Wang, Lei Pang, Gang Li, Chunmiao Yuan

Bibliographic record

VenueJournal of Loss Prevention in the Process Industries · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsDalhousie University
FundersDepartment of Science and Technology of Liaoning ProvinceMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaState Key Laboratory of Explosion Science and TechnologyDepartment of Education of Liaoning ProvinceNational Natural Science Foundation of China
KeywordsExothermic reactionIgnition systemCombustionAutoignition temperatureThermogravimetryActivation energyMaterials scienceKinetic energyDifferential scanning calorimetryMinimum ignition energyChemical engineeringThermodynamicsChemistryOrganic chemistryEngineeringPhysics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.207

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)0.0000.000

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 head0.027
GPT teacher head0.253
Teacher spread0.226 · 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 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

Citations19
Published2020
Admission routes1
Has abstractno

Explore more

Same venueJournal of Loss Prevention in the Process IndustriesSame topicEnergetic Materials and CombustionFrench-language works237,207