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Record W2955194516 · doi:10.1130/g46362.1

Physical modelling of the response of reef islands to sea-level rise

2019· article· en· W2955194516 on OpenAlexaff
Megan E. Tuck, Paul S. Kench, Murray R. Ford, Gerd Masselink

Bibliographic record

VenueGeology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReefGeologyOverwashOceanographySea level riseCoral reefSea levelFlooding (psychology)FreeboardCoastal floodCoastal geographyStorm surgeBarrier islandStormShoreClimate change

Abstract

fetched live from OpenAlex

Abstract Sea-level rise and increased storminess are expected to destabilize low-lying reef islands formed on coral reef platforms, and increased flooding is expected to render them uninhabitable within the coming decades. Such projections are founded on the assumption that islands are geologically static landforms that will simply drown as sea-level rises. Here, we present evidence from physical model experiments of a reef island that demonstrates islands have the capability to morphodynamically respond to rising sea level through island accretion. Challenging outputs from existing models based on the assumption that islands are geomorphologically inert, results demonstrate that islands not only move laterally on reef platforms, but overwash processes provide a mechanism to build and maintain the freeboard of islands above sea level. Implications of island building are profound, as it will offset existing scenarios of dramatic increases in island flooding. Future predictive models must include the morphodynamic behavior of islands to better resolve flood impacts and future island vulnerability.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.204
Teacher spread0.187 · 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 designSimulation or modeling
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

Citations49
Published2019
Admission routes1
Has abstractyes

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