STRATEGI PENERAPAN TEKNOLOGI PEMANENAN AIR HUJAN SKALA INDIVIDU RUMAH TANGGA UNTUK MENDUKUNG PERTANIAN PERKOTAAN BERKELANJUTAN
Bibliographic record
Abstract
The main purpose of this research is to develop strategies for applying individual-scale rainwater harvesting technology to support sustainable urban agriculture to support food security in sustainable urban areas. The research was conducted at labaratory Applying Individual Scale Rainwater in Green House Housing Blok A. No. 1, Sialang Munggu, Pekanbaru. This study was conducted for four month, in Juni 2020 to October 2020. The research method used is analytical hierarchy process (AHP).whose analysis is supported using AHP Simon C Barnand United Kingdom (SCBUK) software, as well as establishing one identified single respondent who is considered a representative contributes significantly to recommending the assessment / justification of experts namely one of the lecturers of the Faculty of Engineering of Riau University who has implemented and pursued the field of research implementation of RWH Technology combined with IoT-based Agricultural Technology to support urban agriculture programs. The main results of the research stated that the plan of collaboration between the government and the community occupies the top priority supported by the implementation of successive PAH technology ground tanks, head tanks and hybridization of ground and head tanks supported by factors of land availability, construction costs and operational and maintenance costs.
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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".