Evaluating Spatial Data Infrastructure for Subak Management in Tabanan Regency, Bali, Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This investigation scrutinized the integration of geospatial intelligence in decisionmaking related to Subak management in Tabanan Regency, Bali.Focused on ascertaining comprehensive Subak data, evaluating local government readiness for Spatial Data Infrastructure (SDI) implementation, and constructing a web GIS-based Subak information system, this study was underpinned by a three-pronged methodological approach.Primary data was collected via interviewing Tabanan's Office of Agriculture personnel, supplemented by an in-depth secondary data analysis.Additionally, a web GIS, using QGIS and JavaScript, was developed.Findings revealed a significant deficit in the Office's preparedness for SDI implementation in Subak management due to the unavailability of critical SDI components.Consequently, a web GIS was constructed to facilitate Subak data dissemination.Usability testing suggested this system was 'easy' to use for 42.5% of respondents, while 52.5% found it 'very easy'.This research underscores the importance of Subak data provision and its potential role in decision-making processes for Subak preservation.The study was delimited to the evaluation of SDI in Tabanan Regency, and the development of a web-based GIS was executed using the waterfall method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it