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Record W2563447069 · doi:10.1002/2016jc012257

Vertical structure of pore pressure under surface gravity waves on a steep, megatidal, mixed sand‐gravel‐cobble beach

2016· article· en· W2563447069 on OpenAlexafffund
Tristan B. Guest, Alex E. Hay

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeologyCobblePore water pressureAttenuationGeomorphologyIntertidal zoneSedimentPhase lagGeotechnical engineeringOceanography

Abstract

fetched live from OpenAlex

Abstract The vertical structure of surface gravity wave‐induced pore pressure is investigated within the intertidal zone of a natural, steeply sloping, megatidal, mixed sand‐gravel‐cobble beach. Results from a coherent vertical array of buried pore pressure sensors are presented in terms of signal phase lag and attenuation as functions of oscillatory forcing frequency and burial depth. Comparison of the observations with the predictions of a theoretical poro‐elastic bed response model indicates that the large observed phase lags and attenuation are attributable to interstitial trapped air. In addition to the dependence on entrapped air volume, the pore pressure phase and attenuation are shown to be sensitive to the hydraulic conductivity of the sediment, to the changing mean water depth during the tidal cycle, and to the redistribution/rearrangement of beach face material by energetic wave action during storm events. The latter result indicates that the effects on pore pressure of sediment column disturbance during instrument burial can persist for days to weeks, depending upon wave forcing conditions. Taken together, these results raise serious questions as to the practicality of using pore pressure measurements to estimate the kinematic properties of surface gravity waves on steep, mixed sand‐gravel beaches.

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)0.001
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 score0.530
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.282
Teacher spread0.258 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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