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Record W4388284301 · doi:10.47461/isoes.2023.wu

Analysis of the Effects of Anchorage Failure on the Stability of a Mast Climber System

2023· article· en· W4388284301 on OpenAlexaff
John Z. Wu, Christopher S. Pan, Bryan M. Wimer, Christopher Warren, Ren G. Dong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsConcordia University
FundersNational Institute for Occupational Safety and HealthU.S. Public Health ServiceCenters for Disease Control and PreventionCenters for Disease Control and Prevention Foundation
KeywordsMast (botany)Computer scienceMast cellBiologyImmunology

Abstract

fetched live from OpenAlex

Mast climbing work platforms (MCWPs) have been widely used at construction sites in the United States since the 1980s.The purpose of the current study is to analyze the effects of a failure of an anchorage on the structural stability of a mast climber system using the finite element (FE) method.The FE models were constructed using commercial software (ABAQUS) and based on representative set-ups of anchored MCWPs as specified in the manufacturers' manuals.The simulated MCWP has three anchorages, and one is assumed to fail.The consequences of an anchorage failure were evaluated numerically.Our results show that the failure of an anchorage caused loading redistributions among the remaining anchorages.Although the reaction forces and moments in the supporting feet for the remaining anchorages increased slightly due to the failure of an anchorage, the structure stiffness decreased remarkably due to the anchorage failure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.243
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

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