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Record W2313101346 · doi:10.1061/9780784413272.207

In Situ Localization and Quantification of Sediment Deposits after Dredging and Disposal Interventions in Sydney Harbour, Canada, Using a Dynamic Penetrometer

2014· article· en· W2313101346 on OpenAlexafffundabout
Nina Stark, Bruce G. Hatcher, Matthew Hatcher, Vincent Leys, Achim Kopf

Bibliographic record

VenueGeo-Congress 2014 Technical Papers · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsDalhousie UniversityCape Breton University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCape Breton University
KeywordsDredgingPenetrometerSedimentGeologyNova scotiaHarbourSeabedSeafloor spreadingOceanographyGeomorphologySoil scienceSoil water

Abstract

fetched live from OpenAlex

In late 2011, more than 4.2 million cubic meters of seafloor sediments dredged from the central channel of Sydney Harbour (Nova Scotia, Canada) were relocated to a coastal site near Edwardsville, on the west side of the South Arm of the harbor. Dynamic penetrometer measurements were carried out in October 2012 for rapid geotechnical characterization, localization, and quantification of dredged material deposits, using the lightweight dynamic penetrometer Nimrod. Complementary sediment samples and digital images were taken at selected positions using a small grab sampler and underwater camera. Sydney Harbour is characterized by a large variety of sediment types. Muddy sediments ranging from low to moderate consolidation states were found, as well as compacted fine sands. The dynamic penetrometer results allowed the localization and quantification of deposited dredge disposal material in a cost- and time-efficient manner. The spatial distribution of sediments is in good agreement with a sediment dynamics model by CBCL Limited. However, the deposited sediment layers were thicker than anticipated, being on the order of 10-20 cm, and reaching several tens of centimeters when mixed with finer, muddy deposits.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.795

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)0.0000.000

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations3
Published2014
Admission routes3
Has abstractyes

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