Modeling the Optimal Path of Industrial Pollution Abatement under Tradable Permit and Seawater Cleaning Project in the Bohai Sea
Bibliographic record
Abstract
For a long term the accumulation of pollutants in the Bohai Sea brings great damage to marine ecosystem, huge economic and social losses. This paper sets up a frame work modeling the optimal path of industrial pollution abatement under tradable permit and seawater cleaning project. Three sub-models are set up which reveal pollution-fish stock interaction, industrial pollution abatement under tradable permit, seawater cleaning project, respectively. Then in next session we introduce dynamic optimization problem which combines contains two state equations, which correspond to two state variables fish stock and pollution stock , respectively; four control variables industrial production , volume of pollution abatement , volume of sea water cleaning and catch of fish , respectively. To maximum benefit of fishery, we follow and introduce the optimal solution of four control variables obtained from dynamic optimization.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".