MétaCan
Menu
Back to cohort
Record W2159835956

고속축중계 자료를 이용한 차량하중 다차로재하계수 결정

2011· article· ko· W2159835956 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

Venue한국도로학회논문집 · 2011
Typearticle
Languageko
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsTruckBridge (graph theory)EurocodeWeigh in motionCode (set theory)Computer scienceStructural engineeringTransport engineeringEngineeringAutomotive engineering
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study is to calculate and propose rational multi-lane loading factors for bridge design considering the probability of simultaneous truck passing in adjacent lanes and real truck weights. The probability of simultaneous truck passing is calculated by analyzing video image taken at various locations in highways and national roads. Weigh-In-Motion system data at two locations are used, which is combined with the probability of multiple presence to calculate the multi-lane loading factors for typical 2 lane and 5 lane bridges. Statistical properties of multi-lane loading factors are also calculated assuming that locations for video images and WIM data represent the overall traffic condition in the country. Results are compared with various design codes in the world and they show that the values are between the current Korea Bridge Design Code and AASHTO LRFD specification or Eurocode and are similar to Canadian Code.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.018
GPT teacher head0.169
Teacher spread0.151 · 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