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Record W3139572127 · doi:10.12789/geocanj.2021.48.171

Hutchison Medallist 1. Wave-Dominated to Tide-Dominated Coastal Systems: A Unifying Model for Tidal Shorefaces and Refinement of the Coastal- Environments Classification Scheme

2021· article· en· W3139572127 on OpenAlexaffvenue
Shahin E. Dashtgard, Romain Vaucher, Byongcheon Yang, Robert W. Dalrymple

Bibliographic record

VenueGeoscience Canada · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsQueen's UniversitySimon Fraser University
Fundersnot available
KeywordsGeologySedimentary depositional environmentOceanographyStormCoastal engineeringTidal powerSubmarine pipelineGeomorphologyEcology

Abstract

fetched live from OpenAlex

Coastal depositional systems are normally classified based on the relative input of wave, tide, and river processes. While wave- through to river-dominated environments are well characterized, environments along the wave-to-tide continuum are relatively poorly understood and this limits the reliability and utility of coastal classification schemes. Two tidal shoreface models, open-coast tidal flats (OCTF) and tidally modulated shorefaces (TMS), have been introduced for mixed wave-tide coastal settings. Following nearly two decades of research on tidal shorefaces, a number of significant insights have been derived, and these data are used here to develop a unified model for such systems. First, OCTFs are components of larger depositional environments, and in multiple published examples, OCTFs overlie offshore to lower shoreface successions that are similar to TMS. Consequently, we combine OCTFs and TMSs into a single tidal shoreface model where TMS (as originally described) and TMS-OCTF successions are considered as variants along the wave-tide continuum. Second, tidal shoreface successions are preferentially preserved in low- to moderate-wave energy environments and in progradational to aggradational systems. It is probably difficult to distinguish tidal shorefaces from their storm-dominated counterparts. Third, tidal shorefaces, including both TMSs and OCTFs, should exhibit tidally modulated storm deposits, reflecting variation in storm-wave energy at the sea floor resulting from the rising and falling tide. They may also exhibit interbedding of tidally generated structures (e.g. double mud drapes or bidirectional current ripples), deposited under fairweather conditions, and storm deposits (e.g. hummocky cross-stratification) through the lower shoreface and possibly into the upper shoreface.The development of the tidal shoreface model sheds light on the limitations of the presently accepted wave-tide-river classification scheme of coastal environments and a revised scheme is presented. In particular, tidal flats are components of larger depositional systems and can be identified in the rock record only in settings where intertidal and supratidal deposits are preserved; consequently, they should not represent the tide-dominated end-member of coastal systems. Instead, we suggest that tide-dominated embayments should occupy this apex. Tide-dominated embayments exhibit limited wave and river influence and include a wide range of geomorphological features typically associated with tidal processes, including tidal channels, bars and flats.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.006

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 head0.029
GPT teacher head0.214
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations30
Published2021
Admission routes2
Has abstractyes

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