PELATIHAN PEMBUATAN CAIRAN BUNGA TELANG SEBAGAI BAHAN SEMPROTAN ANTISERANGGA & ANTIBAKTERI ALAMI DAN RAMAH LINGKUNGAN DI KELURAHAN PLALANGAN KECAMATAN GUNUNGPATI KOTA SEMARANG
Bibliographic record
Abstract
Kelurahan Plalangan merupakan salah satu kelurahan di Kecamatan Gunungpati Kota Semarang. Lokasinya yang berada di daerah Kota Semarang bagian selatan, membuat sebagian besar wilayahnya berupa perbukitan yang didominasi oleh hutan dan kebun. Dari seluruh luas wilyahnya, hanya separuhnya yang digunakan sebagai pemukiman dan perkebunan budidaya atau pertanian. Sisanya belum dimanfaatkan dan dibiarkan terbengkalai. Padahal banyak tanaman di area tersebut yang berpotensi dan berdaya guna, salah satunya adalah bunga telang yang berkhasiat sebagai antiserangga dan antibakteri. Selain potensi sumber daya alam, Kelurahan Plalangan juga memiliki potensi sumber daya manusia yang besar karena memiliki jumlah usia produktif yang besar. Namun, sayangnya separuh kelompok usia produktif belum atau tidak bekerja. Melalui kegiatan pengabdian dalam wujud pelatihan pembuatan air rebusan bunga telang sebagai antiserangga dan antibakteri, diharapkan mampu mengoptimalkan potensi sumber daya yang dimiliki oleh Kelurahan Plalangan sehingga mampu menghasilkan produk berdaya guna. Kata kunci: antibakteri, antiserangga, bakteri, bunga telang, serangga
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".