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Record W7001956343

Mejora continua aplicada a las técnicas y procedimientos de la fundición de placas de aluminio en alúmina S.A.

2014· article· en· W7001956343 on OpenAlexaboutno aff

Bibliographic record

VenueDSPACE System - Metalibrary (University of the Coast) · 2014
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Production (economics)Quality (philosophy)Range (aeronautics)Cost reductionProduction cost
DOInot available

Abstract

fetched live from OpenAlex

In this project the mold change process for the preparation of aluminum plates is performed with reference to the new technology implemented by Wagstaff Industries around the world who have implemented technology adjustable cast ingots in countries like Brazil , China, Netherlands, Russia, Australia , South America , Taiwan, United Kingdom, Germany , Croatia , Canada and the U.S., this technology platform for setting iron molds are used and to have 87 different sizes that can be used as variables molds reducing the number of sets of parts to produce a range of sizes of rolled bars or plates . All this in order to develop a high quality in the plates recovered and increased. The plates or ingots produced by this technology has a minimum of curvature at the end surfaces leading to higher and high recovery rates. The high quality of these ingots using this technology generates significant savings and cut operating activities. Given the above and taking into account the issues presented Alumina in terms of increased operating activities due to the use of fixed molds and presents a decrease in recovered, the latter being the indicator by which the process is measured according to the global standard and identifies the loss or gain of the process technology is implemented in the company and thus reflect improvements in the production process.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.002
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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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 designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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