Research on the Factors Influencing the Security of Grain Supply Capacity in Sichuan Province Based on PLS Structural Equation Modelling
Bibliographic record
Abstract
Sichuan Province, distinguished as one of the top performers among China’s 13 main grain-producing provinces, holds unique advantages in the western region. The continuous increase in grain production lays a solid groundwork for upholding the country’s food security. Grounded in five dimensions—social, economic, technological, resource, and environmental—this article establishes a security evaluation system for the grain supply capacity of Sichuan Province, incorporating 14 specific indicators, and utilizes a PLS structural equation model to investigate the diverse factors influencing the security of Sichuan Province’s grain supply capacity. Findings reveal that social and technological advancements directly negatively affect the security of the grain supply capacity, while economic growth, environmental progress, and resource enhancement directly positively influence grain supply capacity security. It also corroborates that a sustainable grain supply capacity necessitates the harmonious development of these five facets, each being essential. As a result, strategies to safeguard the security of Sichuan Province’s grain supply capacity are put forward, aiming to offer decision-making references for strengthening and elevating Sichuan’s grain supply capability and constructing an advanced “Heavenly Granary”.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".