PENGEMBANGAN SMART VILLAGE KAKI LANGIT DENGAN PENGOPTIMALAN WEB INTEGRATIF
Bibliographic record
Abstract
Smart Village is a village that has the ability to use Information and Communication Technology systems in developing the potential for both natural and human resources. Smart Village can indirectly improve the economy of a village, this is supported by the ability of a smart village to communicate the potentials of natural resources outside the village, and provide knowledge or understanding in managing village potential by the villagers. The development of the Kaki Langit Tourism Village is to accommodate people who love their village to work together to carry out their respective activities with TOURISM as a binding knot by prioritizing the value of local wisdom, so that the community will be more prosperous. Likewise, the role of information technology is needed in realizing the skyline tourism village as a smart village pioneer. Kaki Langit Smart Village Development with Integrative Web Optimization can manage data related to kaki langit tourism villages, thus helping visitors in choosing the desired tourist attraction, and applications are developed by integrating web and homestay management applications so as to help visitors in choosing homestays and packages the desired tourism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".