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Record W4411986728 · doi:10.1088/1361-6404/adebc1

Quarter-Earth tunnel motion under gravity and frictional forces

2025· article· en· W4411986728 on OpenAlexaboutno aff
Khalid Alghanim

Bibliographic record

VenueEuropean Journal of Physics · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsMotion (physics)Earth (classical element)Classical mechanicsGeodesyQuarter (Canadian coin)MechanicsAstronomyGeology

Abstract

fetched live from OpenAlex

Abstract This study investigates the motion of a body through quarter-Earth gravity tunnels using three tunnel configurations under both frictionless and frictional conditions. Unlike traditional center-to-center or chord-based models, the quarter-Earth setup introduces a tunnel configuration that explores partial subsurface traversal between two distant surface points, offering a framework for analyzing how geometry and friction jointly govern motion behavior. Key performance metrics, travel time, maximum velocity, traveled distance, and tunnel-wall reaction forces, are analyzed as functions of tunnel geometry. In the absence of friction, one configuration yields the shortest travel time, while another achieves a higher velocity at the cost of excessive reaction forces. When friction is introduced, energy loss limits motion, making the traveled distance the primary indicator of performance. Threshold conditions for motion initiation and arc traversal are also explored, revealing their dependence on tunnel radius and friction level. The study demonstrates that tunnel design plays a critical role in determining motion efficiency and feasibility. It also provides meaningful pedagogical value by introducing Newtonian mechanics through the context of non-uniform gravitational fields, incorporating numerical modeling, energy conservation, and nonlinear dynamics governed by complex equations of motion.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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