Bibliographic record
Abstract
The need of clean water availability is the basic need of human being for living. In case of emergency, the clean water availability is still needed firstly. In this situation, we have to has a practical knowledge on water resource exploration as well as exploitation effectively and efficiently in such away the water utilization are sustainable. The water availability properties such as, quality, quantity, and site are related to the technology for water conservation and exploitation. Basically, the water availability in emergency could be developed based on the hydrological and hydro geological properties of the nearest location. In situ direct water utilization with bad water quality (in case of flooding) could be handled by using tools of water survival kit such as water bag, purification tablet or powder, and purification bottle. The water utilization in short term with limited scale (in case of refugees camp) could be developed by constructing shallow well (dig well or pumping well) if the shallow aquifer are available. The water utilization in medium term could be developed by constructing water conservation and exploitation system such as mini dam, infiltration galleries, spring water conservation, water seepage, and rain water harvesting. Key words : sumber air, darurat, hidrologi, hidrogeologi, konservasi
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".