MétaCan
Menu
Back to cohort
Record W4321380216 · doi:10.23977/jeeem.2023.060101

Influencing factors of elevator safety performance and strategies to strengthen elevator inspection and testing

2023· article· en· W4321380216 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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of Electrotechnology Electrical Engineering and Management · 2023
Typearticle
Languageen
FieldEngineering
TopicElevator Systems and Control
Canadian institutionsnot available
Fundersnot available
KeywordsElevatorChinaRisk analysis (engineering)BusinessEngineeringStructural engineering

Abstract

fetched live from OpenAlex

With the rapid development of China's social economy, people's living conditions are also constantly improving, in China's current urban development, basically all buildings are equipped with elevators to facilitate people's daily travel. However, some problems will inevitably occur during the use of elevators, and elevators due to their characteristics, once there is a problem, there may be casualties. Therefore, in the current social development, more and more attention is paid to the safety performance of elevators, and the elevator must pass the performance test before use, and the safe elevator can be put into use. However, the performance testing of elevators also has certain influencing factors, and it is precisely these influencing factors that lead to the unstable detection of elevators, so in the current social development, it is necessary to strengthen the inspection and testing of elevators to ensure the safety and stability of elevators in use.

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: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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