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Record W2218264515 · doi:10.2110/jsr.2015.91

The Removable-Cap Suction Corer: An Inexpensive and Durable Device To Extract Unconsolidated, Wet Sediments

2015· article· en· W2218264515 on OpenAlexafffund
Alina Shchepetkina, Murray K. Gingras, S. George Pemberton

Bibliographic record

VenueJournal of Sedimentary Research · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsGeologySuctionGeotechnical engineeringGeochemistryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract: Obtaining sediment samples in unconsolidated sediment presents many challenges. The locale substrate can be soft, penetration of the sediment may be limited, preservation of the sedimentary fabric may be incomplete, and extraction of the sample may be difficult. Some of these issues are overcome with vibracore or suction-core techniques. Vibracoring requires heavy, bulky, and relatively expensive equipment which is costly to ship to the remote field-study locations and difficult to use in muddy settings such as muddy tidal flats or coastal marshes. The Van der Staay suction corer (Van de Meene et al. 1979) is cost-effective and fixes logistical problems, but sample extraction has to be done in the field. The TESS-1 suction corer (Mendez et al. 2003) was designed on the basis of the Van der Staay suction corer and allows recovering intact sediment samples for further laboratory analysis, but it requires custom machinery with more man-hours and uses small-diameter core tubes with a high possibility of disturbance of sedimentary structures in unconsolidated and waterlogged deposits. Here we present a new removable-cap suction corer that is inexpensive to construct, light in weight, highly portable, and can be designed to extract any core diameter. The corer can be used for coarse- and fine-grained wet sediments (tidal flats, riverine and estuarine bars, coastal marshes) and shallow subaqueous environments (lakes, rivers, shallow shoreface).

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)0.003
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: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.139
GPT teacher head0.362
Teacher spread0.223 · 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

Citations26
Published2015
Admission routes2
Has abstractyes

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