The study on the impact of green cultivation and processing technologies on carbon emissions of Hangbai chrysanthemum
Bibliographic record
Abstract
In today's era, agriculture is emitting more and more carbon dioxide. This study focuses on several green planting and processing methods to analyze the impact of green cultivation and processing technologies on chrysanthemum emission reduction. Carbon emissions from traditional agriculture mainly come from fertilizers, pesticides, and the use of machines. Improving traditional agriculture to green technologies (such as organic farming, precision farming, or environmentally friendly processing methods) can reduce carbon emissions. These technologies can also make the soil healthier and save resources. Biochar is a material that improves soil fertility and reduces greenhouse gas emissions. Precision farming advocates the rational use of water and fertilizer, which can also reduce waste. In the processing stage, chrysanthemums used to be dried with coal, but now they can be dried with solar dryers or energy-saving equipment, which can reduce chrysanthemum carbon emissions by 25% to 40%. In addition to the effect of reducing emissions, green technologies and methods can also make crops grow better, produce more, and be more environmentally friendly. This study also mentioned that government policy support and subsidies are also critical.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".