Coastal accretion and sea-level rise in the Cuban Archipelago obtained from sedimentary records
Bibliographic record
Abstract
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.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".