Geomorphologic investigations on karst terrain : a GIS-assisted case study on the island of Barbados
Bibliographic record
Abstract
Maintaining a safe water supply is particularly crucial for karst islands such as Barbados. In order to take proper measures to prevent and reduce saltwater intrusion and to safely extract the right fraction of recharge, karst characteristics must first be fully understood. Geomorphologic investigations of karst surface features of the Porters & Trents groundwater catchments (Barbados) employed GIS technologies to explore the development and distribution of sinkhole features. Contour-based digital elevation models, surface geology, lithology, and remote sensing images were incorporated in this investigation. Seventy-six sinkholes were investigated and occupied approximately 1% (0.16 km2) of the total area (16.41 km2) under study. It was found that age of karstification is not related to age of a terrace. The middle terrace was the one found to be most karstified. Yet, degree of karstification within a terrace is age related. Also, cluster density increases with age of coral within the middle terrace. Density of sinkholes within a cluster also increases with age of coral within the middle terrace. Finally, this study shows that sinkhole long axis, cluster elongation direction, sinkhole alignment and karst lineament all have a tendency to a northeast alignment. This supports the idea that underlying coral rock fracture and conduits have a northeast orientation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".