Reduction in the Emission Rate of Greenhouse Gases and the Increase in Crop Production by Using Compost on Marginal Land
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Greenhouse gases dominated by CO2, CH4, CFC, and N2O come from human (anthropogenic) activities. Efforts to increase the production of rice and corn crops require organic and inorganic fertilizers. The use of chemical fertilizers, which can increase greenhouse gas emissions, is higher than that of organic fertilizers. This study aimed to investigate the reduction in the greenhouse gas emission rate and the increase in crop production caused by organic fertilizer from rice straw and cocoa peel, a community-based sustainable development approach based on education. This research used the mixed method, a descriptive and simple experimental design with the following treatments: t0 = without Compost; ta = straw rice compost dosage of 3 t ha-1; tb = cocoa pod husk dosage of 3 t ha-1; Bta = maize crops + without compost (t0); Btb = maize crops + cocoa pod husk compost (tb); Sta = bare soil + without compost (t0); Stb = rice crops + straw compost (ta); Stc = rice crops + cocoa pod husk compost (tb); and Std = rice crops + without compost (t0). The application of compost reduced agricultural waste and greenhouse gas emissions of CH4 and N2O in both maize and rice fields. Greenhouse gas emissions were reduced by 30 percent compared to those under the application of chemical fertilizers. The utilization of compost as organic fertilizer also increased the production of corn and rice crops compared to that without the application of agricultural waste up to 10.3 tons per ha.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it