Evaluation and Prediction of Regional Water Resources Carrying Capacity: A Case Study of Shandong Province
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Growing pressure on the world’s water resources is having major impacts on us. In this paper, we discuss on water resources carrying capacity. We have a case study of Shandong Province which is one of the most arid regions in China. Considering the dynamics of water supply and demand, we combine the Falkenmark indicator and the binary dynamics model to establish an evaluation model of regional water resources carrying capacity. According to the result of our model, Shandong Province is heavily exploited. The per capita water resources in Shandong province were less than 300 m3 in the past ten years. The increasing destruction and increasing waste make the situation even worse. Then ARIMA model and BP neural network is combined to propose a prediction model. We use it to predict the supply and demand of water resources in Shandong Province in the next 15 years
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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 | 0.005 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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