ANALYSIS OF THE AVAILABILITY OF WATER RESOURCES AND CONSERVATION EFFORTS SUB DAS LESTI DISTRICT OF MALANG
Bibliographic record
Abstract
Lesti sub basins is one of the upstream part of the Brantas river basin located in the district of Malang. Conditions Lesti sub-basins have been damaged thereby potentially experiencing a water deficit. The deficit of water in the dry season in 2017 amounted to 2.141.057 m³ and 2023 amounted to 3.881.593 m³. To overcome these deficits conservation efforts both vegetation and mechanically. Area of land required until 2023 with the planting of agarwood trees covering an area of 18,27 km², covering an area of 15,53 km² and a bamboo plant Poran 51,75 km². Needs rainwater harvesting roofs media as much as 3 sump capacity of 24 m³. Embung needs as much as 3 each reservoir capacity of 800.000 m³. Embung investment costs Rp. 207 205 545 000, - done in 2017 until 2018. Financial aspects of the construction of the water reservoir with an interest rate of 7% per year IRR = 13,19%> 7%; BCR, i (7%) = 1,34> 1 and NPV, i (7%) = Rp. 91.152.353.632,-. The construction of such Eligible.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".