Decision Support System Module for Sustainable Land Development for Thailand: A Case of Chonburi Province
Bibliographic record
Abstract
A decision support system (DSS) module for sustainable land development for Thailand: A case of Chonburi province was developed for decision makers based on available geographic information databases and overlaying techniques available on an internet network. Chonburi province in the Southeast Coast of Thailand was assigned as a special economic development zone or Eastern Economic Corridor (EEC) is a fast growing area for industrial and infrastructure developments causing land use conflicts between privates and governments that were observed elsewhere. Databases including administrative boundary, land resources, land uses, national policies and legislation aspects were integrated for land suitability, condition and limitation for land developments. The system employed ArcGIS Geo-processing service module available on the Central Relation Database that can be accessed via Web Services and RSS. The decision makers could access from the Web Browser and make decision under three conditions, by screening areas for specific land use types, analyzing land use limitations and conditions or for maximum land use benefits. The developed DSS module on land resources spatial analysis and legislation limitations would be a simple technological tool to preliminary and fast selection of proper land managements in the future and would be able to apply in other parts of the country.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".