Coastal Benthic Habitat Mapping to Support Marine Resource Planning and Management in St. Kitts and Nevis
Bibliographic record
Abstract
Abstract A benthic habitat mapping method was applied in St. Kitts and Nevis to create baseline data that serves as input for a marine resource management framework. High resolution satellite imagery (<4 m pixel), combined with an extensive field survey, facilitated the creation of the first high resolution benthic habitat maps for the coastal waters of St. Kitts and Nevis. We demonstrate how Small Island Developing States (SIDS) with limited resources, can employ a scientifically sound, yet relatively low‐cost method to develop coastal benthic habitat maps. These data, along with other marine use information, were reviewed through stakeholder involvement and fed into a larger project aimed at drafting a federation‐wide multi‐objective marine zoning plan. The benthic habitat data quantified the spatial extent and location of key marine ecosystems and served as one of the critical data layers used in the marine zoning decision‐support software. The modeled outputs provided insight to marine resource managers making decisions on how to balance both environmental and economic needs.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".