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SENSOR DE TEMPERATURA VIA RASPBERRY PARA TRANSPORTE DE REFRIGERADOS

2018· article· pt· W2841386451 on OpenAlexaff
Thiago de Almeida Correia

Bibliographic record

VenueInterfaces Científicas - Exatas e Tecnológicas · 2018
Typearticle
Languagept
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsImpact
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

O setor de transporte de refrigerados e congelados aumentam o lucro e fluxo para o comércio. As particularidades no transporte de carga refrigerada, geralmente tratam de produtos que podem sofrer qualquer tipo de deterioração, devido a climatização do ambiente não ser monitorada durante o transporte. A proposta do artigo é desenvolver um protótipo de monitoramento de temperatura e umidade para o transporte de refrigerados em geral. O protótipo apresentado utiliza componentes da nova revolução da computação, a Internet das Coisas, viabilizando para uma solução de baixo custo.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.036
GPT teacher head0.299
Teacher spread0.263 · 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.

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
Published2018
Admission routes1
Has abstractyes

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