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Record W4400617112 · doi:10.23977/jemm.2024.090201

Design study of a mobile snow and ice removal unit for low-rise buildings

2024· article· en· W4400617112 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsSnowUnit (ring theory)Environmental scienceSnow removalMeteorologyGeography

Abstract

fetched live from OpenAlex

Roof snow removal is an essential task in places with high snowfall, such as Northeast China. The aim of this study is to design a movable roof snow removal device with a programme design for the characteristics of low houses. Through field investigation, key parameters such as house height and structural characteristics were determined, and the general scheme of the roller brush snow removal device was designed. The device adopts a triangular frame design, equipped with a roller brush height adjustment mechanism and a travelling system, which enables the rotation and telescopic adjustment of the roller brush in the process of snow removal. The design of the mechanical unit includes the overall frame design, adjustment system design, telescopic system design and transmission system design. Finally, the comprehensive performance of the device is verified by simulated environment test and dynamic simulation, and its application prospect in the field of roof snow removal is expected.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 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
Published2024
Admission routes1
Has abstractyes

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