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Record W4399370096 · doi:10.1139/cgj-2023-0732

Development of a speargun projectile penetrometer in soil

2024· article· en· W4399370096 on OpenAlexvenueno aff
Junlin Rong, Majidreza Nazem, Shiao Huey Chow, Annan Zhou, Sara Moridpour

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Launch and Propulsion Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPenetrometerGeotechnical engineeringProjectileGeologyForensic engineeringEnvironmental scienceEngineeringSoil waterSoil scienceMaterials science

Abstract

fetched live from OpenAlex

This study investigates the applicability of a speargun projectile penetrometer (SPP) for offshore site investigation purposes using experimental testing. The SPP can shoot penetrometers using an elastic/pneumatic force. The speargun is easy to handle, can generate significant kinetic energy, and requires a single operator to perform the test. A laboratory test platform was utilised to hold the SPP and ensure vertical launch, while a laser displacement sensor was employed to monitor the penetration depth and record the corresponding time history. The result demonstrated that the speargun-launched method achieves significantly higher embedment depth compared to an equivalent free-falling penetrometer. In particular, the new method produces sufficiently deep penetration in dense sand, thus overcoming the typical penetrometer tilting issue caused by shallow penetration. These findings provide a preliminary insight into the SPP, allowing for exploration beyond the boundaries of traditional penetrometers in laboratory testing and offshore site investigation.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.201
Teacher spread0.194 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

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