Laju Infiltrasi dan Permeabilitas Tanah pada Areal Kampus I Universitas Negeri Gorontalo
Bibliographic record
Abstract
Land utility for physic buildings on Gorontalo State University campus I has shown rasing significant trends. Whereas, the land was originally rice field productively and water catchments area. Consequently, its function is reduced due to the infiltration of water hampered. This study aimed to (a) determine the amount of soil infiltration rate, and (b) determine the amount of soil permeability. The study was conducted on six months in the campus 1 Gorontalo State University areas. The equipment consists of Guelph permeameter, rol meter, water bag, stop watch, soil bor and raffia. s, the materials consist of water and soil samples. Infiltration measurements carried out in a transect from the south to the north lines. Measurements will be performed at every five meters with two measurements (0-10 cm and 10-20 cm). On existing lines any building or standing crop, the measurement will be carried out on one side to detect the effect of distance and the soil variability. Parameters observed include water infiltration, and soil permeability. The result of this research shown that infiltration rate (i) and soil permeability (Ks) at campus 1 Gorontalo State University areas classified as very rapid. s, the highest of infiltration rate and soil permeability values was to 140 m distance or point 28 and the lowest was to 170 m distance or point 34.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".