Stage of Potential Identification Irrigation Channel Topography Analysis for Micro-Hydro Power in the Kalibawang Irrigation Primary Channel, Yogyakarta, Indonesia
Bibliographic record
Abstract
This study was designed to determine the stages in the identification of micro-hydro in irrigation channels based on the classification and level of data requirements in a project, starting from the initial study, feasibility study and detailed engineering design. The study was conducted with site selection criteria using four information systems and technology tools, namely Google Earth, GIS Topography, UAV Drone Phantom DJI 4, and Nikkon DTM 332 Total Station. The results shows through GE and GIS, obtained 23 potential points, 7 of which are high potential, followed by field measurements with 1 selected UAV location Cascade, and detailed with TS to produce Head (H) 12 m, with CM and FDC probability 75% discharge (Q) 5.5 m3/s, generated power (P) 550 kW. This study provides a method and solution for speed in identifying potential with Google Earth and GIS (Macro Class), speed and risk reduction for surveyors with UAVs (Mezo Class), and accuracy and detailing at selected locations with Total Station (Micro Class). So that this research provides accuracy in the stages, methods and tools used in the identification of micro-hydro potential in irrigation channels.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".