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Record W3034939574 · doi:10.1126/sciadv.aay3656

Coral reef islands can accrete vertically in response to sea level rise

2020· article· en· W3034939574 on OpenAlexaff
Gerd Masselink, Eddie Beetham, Paul S. Kench

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsSimon Fraser University
FundersEngineering and Physical Sciences Research CouncilMinistry of Business, Innovation and EmploymentRoyal Society Te Apārangi
KeywordsReefCoral reefCoralOceanographySea level riseAccretion (finance)AtollGeologyLandformEnvironmental issues with coral reefsGeomorphologyClimate change

Abstract

fetched live from OpenAlex

Increased flooding due to sea level rise (SLR) is expected to render reef islands, defined as sandy or gravel islands on top of coral reef platforms, uninhabitable within decades. Such projections generally assume that reef islands are geologically inert landforms unable to adjust morphologically. We present numerical modeling results that show reef islands composed of gravel material are morphodynamically resilient landforms that evolve under SLR by accreting to maintain positive freeboard while retreating lagoonward. Such island adjustment is driven by wave overtopping processes transferring sediment from the beachface to the island surface. Our results indicate that such natural adaptation of reef islands may provide an alternative future trajectory that can potentially support near-term habitability on some islands, albeit with additional management challenges. Full characterization of SLR vulnerability at a given reef island should combine morphodynamic models with assessments of climate-related impacts on freshwater supplies, carbonate sediment supply, and future wave regimes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.249
Teacher spread0.228 · 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 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

Citations110
Published2020
Admission routes1
Has abstractyes

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