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Record W2790930304 · doi:10.1155/2018/5937560

Development and Testing of Intelligent Alcohol Transportation Security System

2018· article· en· W2790930304 on OpenAlexvenueno aff
Velaphi Msomi, Shandene E. A. Solomons

Bibliographic record

VenueJournal of Advanced Transportation · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsnot available
Fundersnot available
KeywordsContainer (type theory)Drop (telecommunication)Security systemComputer securityShort Message ServiceIntelligent transportation systemEngineeringComputer scienceAutomotive engineeringWaste managementTransport engineeringTelecommunicationsMechanical engineering

Abstract

fetched live from OpenAlex

The development and testing of intelligent liquid transportation security system are being reported in this paper. The targeted fluid to be secured was ethanol alcohol and this was due to the theft cases occurring during the transportation of this product from the supplier to the customer. The system was developed such that only the radar level sensor (VEGAPULS 62) might be in contact with the fluid and the rest of the system remained outside the liquid carrying container to be secured. The system was developed such that it reports any abnormal liquid level drop through short message service (SMS). The functioning of the developed system was tested through the use of 1040 L Intermediate Bulk Container (IBC) filled with water which was hauled for about 1.5 km. The liquid theft was simulated and the system sent two SMS. The first SMS reported the beginning of water level drop and the second one reported the ending of water level drop. The second SMS reported the amount of liquid that was taken out of the container.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.177

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 head0.026
GPT teacher head0.246
Teacher spread0.219 · 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 designObservational
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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