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Record W7098235357

LONG-TERM GOAL

2016· article· en· W7098235357 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsPenetrometerPenetration testGeotechnical investigationSedimentWork (physics)Quasistatic process
DOInot available

Abstract

fetched live from OpenAlex

The long-term goal of this work is to develop a set of tools that can be used to determine insitu values for certain sediment geotechnical properties that are needed to predict the potential for burial when various objects such as mines impact the sea bottom. OBJECTIVES The principal objective of this work has been to develop a set of mechanical, remote-sensing tools that will permit the determination of the main geotechnical variables that control mine burial in the bottom. These variables include the shear strength of both soft, fine-grained sediments and coarser, dilative granular sediments. A further objective is to understand better the response of typical near-shore deposits composed of layered fine and coarse sediment. APPROACH Over the past several years our field work has led to the development of a number of remote-sensing tools that have direct application to the problem of mine burial prediction. As an example, the penetration resistance measured by several different types of probe that we have developed is directly related to the bearing capacity of the sediment which is of prime importance in studies of mine burial in the seafloor. These probes include XBP, an expendable bottom penetrometer, PROBOS, a modified version of the Canadian STING penetrometer and a quasistatic penetrometer, STATPEN, that measures both cone and sleeve penetration resistance to a depth of 2 meters into the sea floor. The XBP probes have been used to map critical areas in recent NATO exercises aimed at “Rapid

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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