Study on the Dynamics Simulation of Quantity Change in Cultivated Land Resources in Chongqing City
Bibliographic record
Abstract
This paper uses system dynamics software vensim to simulate the relations between all systems of the quantity of cultivated land resources in Chongqing City. By establishing SD model, it analyzes how the change in the quantity of cultivated land resources influences other systems and how other sub-systems influence the change in the quantity of cultivated land resources. It sets the parameters in the model with the historical data during the years 2002 to 2010 in Chongqing. After a historical examination on them, this paper uses parameter levels in different projects for a simulation influencing analysis. That is, if the cultivated land is used in a large quantity, the reduction speed of the quantity of cultivated land will, instead, be accelerated; and if the per unit yield of grain is increased, the demand on cultivated land will present a linear reduction state.
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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 | 0.000 | 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 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".