MétaCan
Menu
Back to cohort

SSOC Application Research of Electric Heat Tracing Intelligent Control System

2025· article· en· W4414126201 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Physics Conference Series · 2025
Typearticle
Languageen
FieldComputer Science
TopicTechnology and Security Systems
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsTracingElectric heatingIntelligent controlElectricityControl systemTemperature controlElectric lightEnergy consumption

Abstract

fetched live from OpenAlex

Abstract In south sulige area, the traditional electric heat tracing technology adopts manual inspection to judge whether its working performance is normal or not, which has great security risks. Based on sensor acquisition, intelligent temperature control and remote communication technology, an intelligent control system of electric heat tracing is established. The collected information is processed by algorithm and communicated with the upper computer to realize the intelligent management of electric heat tracing system. The intelligent control system of electric tracing heat can realize the functions of electric tracing tropical operation data collection, data analysis, remote control, emergency shutdown, fault alarm and historical query. The first application of the electric heat tracing intelligent control system in the Sunan C4 gas gathering station of the Sulige South block has achieved digital management of the electric heat tracing belt. It can carry out real-time monitoring, remote control and intelligent temperature control of the running status of the electric tracing belt, increase the service life of the electric tracing belt and achieve the goal of energy saving and consumption reduction at the same time.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.306
Teacher spread0.277 · 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