Analisis Laju Infiltrasi Berbagai Penggunaan Lahan di Desa Kaligending, Karangsambung, Jawa Tengah
Bibliographic record
Abstract
Penentuan laju infiltrasi bertujuan untuk menentukan laju masuknya air kedalam tanah. Dimana penentuan ini di uji dari berbagai penggunaan lahan dengan memperhatikan faktor infiltrasi seperti tekstur tanah, litologi batuan, vegetasi penutup, dan kemiringan lereng. Model yang di uji bersifat empiris, dan merupakan fungsi persamaan tergantung waktu, dengan menggunakan model Horton. Infiltrasi sangat menentukan berlangsungnya proses daur hidrologi yang terjadi di suatu daerah. Dimana infiltrasi adalah proses masuknya air kedalam tanah baik dari air hujan maupun irigasi. Laju infiltrasi dengan nilai kecil kemungkinan limpasan permukaan mempunyai nilai besar. Pada kegiatan pertambangan nilai laju infiltrasi berpengaruh pada kondisi tanah selain itu juga berpengaruh pada limpasan air yang terjadi pada area pertambangan. Penelitian ini akan dilakukan di Lipi Karangsambung tepatnya di Desa Kaligending, Karangsambung, Kebumen, Jawa Tengah. Data infiltrasi diperoleh dengan menggunakan alat infiltrometer jenis Guelph Permeameter. Pada akhirnya diketahui peta persebaran laju infiltrasi menggunakan metode IDW dari perangkat lunak Arcgis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".