Multitemporal Geomorphological Analysis of Beach-Cliff Systems in Seismic Zones: A Case Study of Manabi, Ecuador
Bibliographic record
Abstract
Coastal regions are increasingly recognised as prime locations for recreational tourism.Among these, the province of Manabi, Ecuador, with its distinct coastal cliff geomorphology, has considerable tourist appeal.However, a major seismic event in 2016, registering Mw 7.8, resulted in significant geomorphological alterations at the coastal level.This study sought to characterise the geomorphology of beach-cliff systems in Manabi province, utilising Geographic Information Systems (GIS) to identify the geomorphological changes induced by the 2016 earthquake, with the objective of informing sustainable management strategies.GIS was employed to discern the geomorphological alterations in the beach-cliff systems and compute their retreat rates.A Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis facilitated understanding of the current state of the cliffs, critical for devising coastal and tourism management strategies.The study area encompassed the beach-cliff systems of Canoa, Bahia de Caraquez, Crucita, Santa Marianita, San Lorenzo, and Los Frailes, with coastal, gravitational, and fluvial dynamics identified.Comparisons of pre-and post-earthquake conditions revealed notable changes, including surface flows and uplifts.Calculated cliff retreat rates (in m/year) yielded values of 0.76 (Canoa), 0.89 (Bahia), 1 (Crucita), 0.53 (Sta.Marianita), 0.83 (San Lorenzo), and 0.62 (Los Frailes).The study concludes with proposed strategies centred on political, scientific-academic, social, and tourism sectors to foster sustainable local development.These findings underscore the importance of comprehensive geomorphological assessment in the context of natural disasters, and its significance in informing management strategies for sustainable tourism and development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".