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Record W2616607572 · doi:10.4172/2168-9806.1000160

From the Mining to the Obtaining of Ferroelectric Ceramics

2017· article· en· W2616607572 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Powder Metallurgy and Mining · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCold Fusion and Nuclear Reactions
Canadian institutionsnot available
FundersMinisterio de Ciencia, Tecnología y Medio AmbienteConsejo Nacional de Ciencia y TecnologíaUniversité Laval
KeywordsCeramicFerroelectric ceramicsFerroelectricityMaterials scienceMineralogyEnvironmental scienceMetallurgyGeologyDielectricOptoelectronics

Abstract

fetched live from OpenAlex

Enjoy and learn by looking for the ancient techniques of working the pottery from the clay, led to the obtaining of more convenient materials and better mineral extractive processes. This is what has allowed the development of the great variety of domestic ceramic materials and the emergence of electro technical ceramic. The piezoelectric and ferroelectric ceramics depend on the origin of their raw materials, what has been the process of obtaining them? As well as on the variations of the ceramic process used. Five examples are presented of how the reagents, their purity, granulometry and present phases can modify the properties and characteristics of ferroelectric ceramics. The work will make a panoramic visualization of the materials and their origins, arriving at the obtaining of ferroelectric ceramics and the influence of the raw material (obtaining process) and the properties of the desired product.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.029
GPT teacher head0.251
Teacher spread0.222 · 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