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Record W4416279355 · doi:10.3389/fmars.2025.1700038

Coastal dynamics and geomorphology of the Pozos Colorados sector, Santa Marta: an analysis of sea level rise and paleo-shoreline

2025· article· en· W4416279355 on OpenAlexfundno aff
Diego Villate-Daza, Eduardo Guimarães Barboza-Pinzon, Bismarck Jigena-Antelo, Juan José Muñoz Pérez, Giorgio Anfuso, Luana Portz, Fernando Afanador-Franco, Rogério Portantiolo Manzolli

Bibliographic record

VenueFrontiers in Marine Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
FundersUniversidade Federal do Rio Grande do SulCanadian Academy of Sport and Exercise Medicine
KeywordsShoreAccretion (finance)Coastal erosionSea level riseGround-penetrating radarErosionClimate change

Abstract

fetched live from OpenAlex

Introduction We assess how inherited subsurface architecture and human interventions control recent shoreline change and vulnerability in Pozos Colorados (Santa Marta, Colombian Caribbean). Methods We integrated geomorphological mapping, multi-decadal shoreline extraction with CoastSat (1986–2024; EPR), Ground Penetrating Radar (GPR) profiles, paleoshoreline detection from high-resolution DEM, and paleo/future sea-level scenarios. Results Net shoreline retreat averaged ~–2.0 m yr⁻¹ (max erosion in 1986–1996; localized accretion after 2021), linked to sediment budget shifts and engineering structures. GPR reveals erosional surfaces/terraces guiding present morphodynamics. Paleo sea-level highstands (MIS5e +6 m; mid-Holocene +3 m) and a 2100 SLR scenario overlap low-lying inherited surfaces. Discussion Coupling surface (CoastSat) and subsurface (GPR) evidence clarifies multi-scale controls on instability and maps exposure hotspots, supporting ecosystem-based, adaptive coastal management in this urbanizing sector.

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 score0.350
Threshold uncertainty score0.695

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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