Listrik mikro hidro berdasarkan Potensi debit andalan sungai batanghari kota jambi
Bibliographic record
Abstract
The city of Jambi is an area batanghari river, the study of the utilization of renewable energy, especially hydro power through the tributaries should be considered for the planning of Micro Hydro Power Plant (PLTMH). Water conditions that can be utilized as electricity generating resources have a certain flow capacity and altitude of their drainage system. A major problem in hydroelectric generation is the availability of river water discharge as a propulsion energy. Therefore, certain techniques are needed to predict the potential flow of river water at all times or the river's main discharge that can be used for hydro-electric power generation, no long-term rainfall and critical watershed conditions can cause water flow the stream becomes small and even becomes dry. Manual river flow measurements over time can only represent the volume of river flow at the time of measurement. Changes that occur due to the occurrence of rain at a later time, or decrease in river discharge due to decreased soil water savings, can not be monitored properly it is necessary to plan a continuous discharge for the capacity of generator turbine rotation by calculating the electrical capacity of the generator which can be generated optimally
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.057 |
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".