MétaCan
Menu
Back to cohort
Record W4386494030 · doi:10.1117/12.2689772

Transportation structure monitoring technology in a steel company based on intelligent identification

2023· article· en· W4386494030 on OpenAlexaboutno aff
Zhenkai Xie, Kai Shi, Hailiang Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicE-commerce and Technology Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsIdentification (biology)Quarter (Canadian coin)Quality (philosophy)Production (economics)Automotive engineeringEngineeringTransport engineering

Abstract

fetched live from OpenAlex

Based on intelligent identification technology, a system for transportation structure management of a steel company is designed and developed, which is used to monitor the status and analyzed the behavior of vehicles used by the steel company. According to the statistics of vehicles used by the steel company for one year, the results show that the vehicle structure of the steel enterprise is stable, and the frequency of use of vehicles accounting for less than a quarter exceeds 95%; The frequency of vehicles use in different seasons shows a regular distribution. Combined with the vehicle status data, the vehicle frequency can be reduced to improve the ambient air quality based on ensuring the production activities of the company

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
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 head0.265
Teacher spread0.238 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicE-commerce and Technology InnovationsFrench-language works237,207