Bibliographic record
Abstract
Abstract Aim The purpose of this study is to apply geographical information and artificial neural network (ANN) technologies in assessing ecosystem distribution on the island of Saint Lucia, as well as to develop an improved ecological classification using Holdridge’s system of natural life zones. Location Saint Lucia is a Caribbean island state located at 14°N and 61°W and of a land area of 616 km2. Methods The main inputs for classifying life zones were a 25‐m × 25‐m digital elevation model of Saint Lucia (DEM), mean annual temperature and annual total precipitation. The DEM was initially obtained by digitizing contour lines on a topographic map. Elevation–temperature regressions developed for Puerto Rico were used to generate point‐estimates of mean temperature across the island of Saint Lucia. A generalized (trained) ANN was employed to create an annual total rainfall surface for the island. The variables of longitude, latitude and elevation were used to construct the rainfall model. Comparison of predicted and observed total precipitation revealed that the ANN explained over 95% of variability exhibited in the observed data, within a standard error of estimate of 123 mm (~6% of the total precipitation). Results Three complete and three transitional life zones were identified as occurring on Saint Lucia. Twelve per cent of the island was classified as tropical premontane moist/wet, 20% as tropical premontane wet, 6% as subtropical dry/moist, 29% as subtropical moist, 26% as subtropical moist/wet and 7% as subtropical wet. Conclusion Quality of life zone delineation depends on an objective application of universally accepted criteria and available terrain analysis technologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".