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Record W3025904190 · doi:10.1177/0959683620919981

Coastal accretion and sea-level rise in the Cuban Archipelago obtained from sedimentary records

2020· article· en· W3025904190 on OpenAlexaff
Misael Díaz-Asencio, Maickel Armenteros, José Antonio Corcho Alvarado, Ana Carolina Ruíz-Fernández, Joan-Albert Sánchez-Cabeza, Adrian Martínez-Suárez, Stefan Röllin, Vladislav Carnero-Bravo

Bibliographic record

VenueThe Holocene · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAccretion (finance)Sedimentary rockOceanographyOverwashArchipelagoGeologyClimate changeSea levelDredgingSedimentPhysical geographyShoreEnvironmental scienceGeographyGeomorphologyPaleontologyBarrier island

Abstract

fetched live from OpenAlex

Sea-level rise (SLR) is one of the most pervasive consequences of global warming, and the Cuban Archipelago is threatened by current and future SLR. In order to support adaptation plans, it is essential to have reliable information about sea-level change during the last decades at the local scale, particularly in the most vulnerable regions. Here, we use sedimentary records to evaluate coastal accretion and to estimate the relative sea-level rise (RSLR) in two vulnerable coastal sites in central Cuba: Cayo Santa María (CSM) and Península de Ancón (PA). Both sites showed sediment sections with a continuous record of sediment accretion as a result of relative SLR and tropical storms. The sedimentary process was different between CSM and PA owing to differences in geomorphology and primary mineral composition. Sedimentary records also showed recent impacts of anthropogenic activities, likely increasing the vulnerability of the shoreline to SLR. The estimated RSLR values agreed with tidal gauge records, although they spanned a much longer time period (CSM: 0.5 ± 0.1 mm a −1 , span of 38 years; PA: 1.5 ± 0.3 mm a −1 , span of 92 years). Our results confirm that this methodology may be used to estimate the RSLR in places where data by instrumental records do not exist.

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)0.000
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 score0.324
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.025
GPT teacher head0.212
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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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