PEMANFAATAN AQUAPONIK SEBAGAI TEKNOLOGI BUDIDAYA IKAN NILA DAN SAYURAN YANG MENDUKUNG PERTANIAN BERKELANJUTAN DI DESA KAYANGAN, KECAMATAN KAYANGAN, KABUPATEN LOMBOK UTARA
Bibliographic record
Abstract
This introduction to aquaponic planting techniques aims to provide examples of the application of advanced and sustainable agricultural technology to the people in Kayangan village, Kayangan sub-district, North Lombok district (KLU). This activity was carried out from December 2022 to February 2023 at the Bagek Kembar hamlet, Kayangan. The fish used were 60 days old fish seeds with a size of 10 cm x 5 cm which were reared in a 4x3 m tarpaulin container. The plant growth media used is in the form of used foam taken from one of the residents' houses with a size of 5 cm. Placement of the planting media uses used plastic cups and used bottles as containers for placing plastic cups. The vegetables used in making this aquaponics are mustard greens, lettuce and pakcoy. The nutrients used to support plant growth are in the form of fish manure and some additional organic nutrients which are made independently using organic waste from the kitchen. The results of using aquaponic technology as a cultivation technology show that the plants grow but the leaves of the plants turn yellow and the fish in the pond can grow large.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".