Evolutifon of decision support system architectures: applications to land planning and management in Cuba
Bibliographic record
Abstract
The main objective of this paper centers on reviewing the evolution of Decision Support System´s (DSS) architectures, particularly as they apply to natural resources. Today it is difficult to conceive the existence of a rural planning automated system that doesn't include spatial analysis functionality and that does not consider the integrated use of different analytical modules. This wider range of functions allow for solving problems from resource and environmental management. Geographical Information Systems (GIS), automated land evaluation, multi-criteria participatory analysis in decision making are but the most salient technologies in a DSS. DSS have evolved; their architecture, mode of implementation, as well as their functionality and the incorporation of new computational techniques have advanced lately. In the particular case of Cuba,the first steps in materializing this evolution have begun. At present, the National Sugar Cane Research Institute (INICA)leads a research project oriented towards the development and building of a dedicated DSS for sugar cane cropping.This is conceived as an integrated SDSS (Spatial DSS)to support decision -making and multiple problem- solving in such a fundamental productive activity such as sugar cane agriculture in Cuba.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".