Bibliographic record
Abstract
The carbon emissions of the food industry account for a significant proportion of the overall carbon emissions. Figuring out the carbon footprint of the food industry is a great way to find out the reason for the large amount of carbon emissions. In this part, the paper selected several variations that may have some influence on carbon emissions and did some intensive studies to figure out which one is the major factor. By analyzing through life cycle assessment and regression by Random Forest, it was figured that fertilizer is the most influential factor in carbon emissions, and focusing more on the usage of compound fertilizer and decreasing the usage of other kinds of fertilizer can effectively reduce the carbon emissions. Then the paper tried to figure out whether the reduction is realistic, and the paper did research in the financial field. The paper did an ESG score ranking of the fertilizer company, cultivated the investment weight in the portfolio, and did a risk rating of them. The research adopted the Grey Forecasting Model and quadratic Optimization Formulation to process data. Through the simple study of the carbon footprint of fertilizers and an analysis of the investment ratio of investors, it is finally found that to economically promote and support the development of carbon-reducing compound fertilizers by business is essential. Besides, it coincides with investors’ interests.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".