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Competent<i>vs</i>. Observed Grain Size on the Seabed of the Gulf of Maine and Bay of Fundy

2017· article· en· W2606512005 on OpenAlexaff
Paul S. Hill, Shaun Gelati

Bibliographic record

VenueJournal of Coastal Research · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBaySeabedSedimentGeologyGrain sizeOceanographySediment transportGeomorphology

Abstract

fetched live from OpenAlex

Hill, P.S. and Gelati, S., 2017. Competent vs. observed grain size on the seabed of the Gulf of Maine and Bay of Fundy.The output of a three-dimensional tidal circulation model and nearly 10,000 sediment samples are used to compare observed and competent grain sizes on the floor of the Gulf of Maine and Bay of Fundy. Competent grain size is the largest grain size a flow is capable of mobilizing. Competent and observed grain sizes have similar broad spatial distributions. Coarser observed grain sizes are found in regions of larger stress, and associated coarser competent grain sizes and finer observed sizes are found in regions with finer competent sizes. Areas in which competent sizes are finer than observed sizes likely have significant sources of seabed stress that are not included in the model, specifically from waves and subtidal flows. Areas in which competent sizes are coarser than observed sizes likely are regions where sediment input into the region overwhelms the ability of near-bed flows to transport sediment away from the region, leaving the seabed with a texture similar to that of the supply. The results indicate that sediment texture is unlikely to change greatly if large-scale tidal power development is pursued in Minas Passage, which connects the Minas Basin to the Outer Bay of Fundy. Forecast changes of sediment texture in the Gulf of Maine are small, and in the Bay of Fundy, sediment texture is unlikely to change because it is dominated by sediment supply, which should not be affected by tidal power development.

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)0.002
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 score0.058
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 science0.0010.000
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 head0.077
GPT teacher head0.300
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

Citations6
Published2017
Admission routes1
Has abstractyes

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