Multiple Regression Analysis of Factors Influencing Grain Yield
Why this work is in the frame
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Bibliographic record
Abstract
Since ancient times, China has been a major agricultural country and is now the world's top food producer. With the second largest population in the world, it is of great relevance to investigate the factors influencing grain production. In this paper, we look at the main factors that affect grain yield. A simple multiple linear regression analysis was used to develop a model with fertiliser application, sown area, flooded area, farm machinery power and agricultural labour as independent variables and total annual grain production as the dependent variable. The resulting model fitted well and the observations were independent of each other. However, there was serious covariance between the variables, so we tested the model and concluded that the model satisfied the chi-squaredness, but there was more serious covariance between the variables, which affected the model building and could cause model distortion. So finally we build stepwise regression and ridge regression models respectively to eliminate the multicollinearity among the variables in order to optimise the model.
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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 | 0.001 |
| 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) | 0.000 | 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