Pengaruh Cadangan Karbon terhadap Karbondioksida Terlarut berdasarkan Penggunaan Lahan di Kecamatan Cikajang, Kabupaten Garut
Bibliographic record
Abstract
Abstract. The 1.2°C increase in annual surface temperature over the past 20 years has led to climate change and global warming. Greenhouse gases have a huge influence on climate change and global warming. Carbon dioxide as one of the greenhouse gases needs to be reduced in the atmosphere. Ways to reduce carbon dioxide can be done by utilising forests and land cover as carbon stocks as stated in Indonesia's Forest and Other Land Use Net Sink 2030 policy. Cikajang sub-district has various types of land use, but is inseparable from land change. In addition, Cikajang sub-district is also given abundant water for human use. Land use change can affect carbon stocks to water conditions. The purpose of this study is to determine the relationship between carbon stocks and dissolved carbon dioxide based on land use in Cikajang District. The approach methodology used is quantitative using simple linear regression analysis. Total carbon stocks by land use land use in Cikajang sub-district was 704,548.49 tonnes-c. Water conditions on land use of protected forest and production forest is better than other land uses with dissolved carbon dioxide of 4.548.49 tonnes-c. other land uses with dissolved carbon dioxide of 4.4 mg/L. Analysis results simple linear regression analysis showed a significance value of 0.026 <0.05 probability value, and t count (-2.05). probability value, and t count (-2.729) > t table (2.306) meaning that there is an influence of carbon stock coefficient on dissolved carbon dioxide according to type of land use in Cikajang. The value obtained to fulfil the equation of the simple linear regression analysis method Y = a + bX is Y = 9,680 + (-0,746E-005)X. Abstrak. Kenaikan suhu permukaan tahunan dalam 20 tahun terakhir sebesar 1,2°C telah menyebabkan perubahan iklim dan pemanasan global. Gas rumah kaca memiliki pengaruh yang begitu besar bagi perubahan iklim dan pemanasan global. Karbondioksida sebagai salah satu gas rumah kaca perlu ditekan keberadaannya di atmosfer. Cara untuk mengurangi karbondioksida dapat dilakukan dengan memanfaatkan hutan dan tutupan lahan sebagai cadangan karbon seperti yang tertera dalam kebijakan Indonesia’s Forest and Other Land Use Net Sink 2030. Kecamatan Cikajang memiliki jenis penggunaan lahan yang beragam, namun tidak terlepas dari adanya perubahan lahan. Selain itu, Kecamatan Cikajang juga diberikan air yang melimpah untuk kepentingan manusia. Perubahan penggunaan lahan dapat mempengaruhi cadangan karbon hingga kondisi air. Tujuan penelitian ini yaitu mengetahui hubungan antara cadangan karbon dengan karbondioksida terlarut berdasarkan penggunaan lahan di Kecamatan Cikajang. Metodologi pendekatan yang digunakan yaitu kuantitatif dengan menggunakan analisis regresi linier sederhana. Total cadangan karbon menurut penggunaan lahan di Kecamatan Cikajang sebesar 704.548,49 ton-c. Kondisi air pada penggunaan lahan hutan lindung dan hutan produksi lebih baik dari penggunaan lahan lainnya dengan karbondioksida terlarut sebesar 4,4 mg/L. Hasil analisis regresi linier sederhana menunjukkan nilai signifikansi 0,026 < 0,05 nilai probabilitas, dan t hitung (-2,729) > t tabel (2,306) artinya terdapat pengaruh koefisien cadangan karbon terhadap karbondiosida terlarut menurut jenis penggunaan lahan di Kecamatan Cikajang. Nilai yang didapatkan untuk memenuhi persamaan metode analisis regresi linier sederhana Y = a + bX adalah Y = 9,680 + (-0,746E-005)X.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".