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Record W2768605594 · doi:10.1109/iccss.2017.8091381

Research on simulation and application of marine oil purifier system

2017· article· en· W2768605594 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

Venue2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS) · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Algorithms and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMarine engineeringEnvironmental scienceComputer sciencePetroleum engineeringEngineering

Abstract

fetched live from OpenAlex

Aimed at the limitation of the written teaching of oil separator, in order to make the trainees master the operation method of the oil separator system effectively, the Modeling and Simulation Research of Alfa Laval S series oil separator system based on EPC-50 as control unit is put forward. Combined with sequential control to make the realization of automatic control of oil separator function and the reappearance of the failure of the oil purifier, the modeling of oil purifier working process is mainly molded in this article. By using the computer simulation technology, the simulation process of the oil purifier is reconstructed. The simulation results that the simulation system of the oil purifier is basically the same as the actual operation of the oil purifier can be shown. The system can be used for the teaching and training of the oil purifier.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score0.612

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 studies0.0010.000
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 head0.048
GPT teacher head0.344
Teacher spread0.296 · 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