ANALISIS DAYA DUKUNG LAHAN BERDASARKAN KEBUTUHAN DAN KETERSEDIAAN LAHAN PERTANIAN DI KABUPATEN GORONTALO UTARA
Bibliographic record
Abstract
The objectives of this study are: To study and analyze the availability of land in North Gorontalo District, Assessing and analyzing land requirements in North Gorontalo District, Analyzing and evaluating Carrying Capacity Ratio of agricultural land in North Gorontalo Regency. The research method is qualitative which is analyzed descriptively quantitative. The study was conducted covering 11 districts in North Gorontalo Regency. The data used in this study are primary data and secondary data, Data collection was obtained through direct observation at the research location and through literature studies from various trusted sources or related institutions namely the Central Statistics Agency of North Gorontalo Regency 2018, and the Department of Agriculture, Horticulture Plantation, North Gorontalo District 2018. The method for calculating the carrying capacity of land uses the method of analysis in accordance with Regulation of the Minister of Environment No. 17 of 2009. The results showed the availability of land (SL) of North Gorontalo Regency was 59,235,467 Ha, and the land requirement (DL) was 46,893,556 Ha. the value of land availability is greater than the need for land SL ˃ DL, carrying capacity for district level is categorized as surplus. Carrying capacity of agricultural land (CCR) in North Gorontalo District is obtained less than one or CCR <1 that is 0.36, the assumption is that the carrying capacity of agricultural land is deficit. Policy implications for the North Gorontalo District Government should emphasize sustainable agricultural development policies through intensification, extensification and revitalization programs, so that the carrying capacity of agricultural land can be increased in realizing self-reliance and sustainable regional food security.*eprm*
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".