KAJIAN PEROLEHAN KARBON SEBAGAI DAMPAK INTERVENSI KASUS: EKS-PLG BLOK A, MENTANGAI, KALIMANTAN TENGAH (CARBON RESULT STUDY AS INTERVENTION IMPACT, CASE: EKS-PLG BLOCK A, MENTANGAI, CENTRAL BORNEO)
Bibliographic record
Abstract
A number of crop growth simulation models have been developed using the radiation use efficiency (RUE) concept to predict crop growth and yield in various environments. These models generally calculate daily biomass production as the product of the quantity of radiation intercepted and RUE. Besides that biomass production was deterimined by water and nitrogen available factor. So, this research was carried out to quantify the RUE, biomass and leaf area index on Jatropha under rainfall condition, four levels of nitrogen fertilizer (N) and two and three population densities (P) planted twice. The experiments used a systematic Nelder fan design with 9 spokes and 4 – 5 rings were conducted at SEAMEO-BIOTROP field experiment in 2007. Data from the first experiment were used for parameterization and calibration and the second experiment data for model validation. Based on parameterization, we found that RUE can prediction above ground biomass accumulation of Jatropha were 0.94 (r=0.83) g MJ-1 to 1.3 (r=0.75) g MJ-1. Water availability was between ψ=-30 kpa and ψ=-1.5 MPa for field capacity and wilting point, respectively. Nitrogen demand of root, stem, leaf and grain N were (Ndemr=0.75), (Ndems=0.60), (Ndeml=2.53), and (Ndemg=2.41), respectively. Validation showed that model can simulate crop growth and development of Jatropha.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".