Identification of Key Production Factors in China's Environmental Protection Industry Based on Deep Learning
Bibliographic record
Abstract
Analyzing from the micro level of the basic unit of environmental protection industry -- enterprise, this paper takes   relevant information of more than 8000 key enterprises in China's environmental protection industry from 2018 to 2020 as the big data training samples, and uses BP neural network method to identify the key production factors that have great impact on the output of the whole China's environmental protection industry and its main subdivisions. The results show that China's environmental protection industry is still in the growing stage in the current stage, with significant capital pulling, technological innovation driving, and management innovation also plays an important role. There are differences in the performance of water, air and solid waste in the environmental protection industry. In detail, R&D expenses from the government plays the most important role in water pollution prevention and control industry. Technology innovation plays the most important role in air pollution prevention and control industry. And staff play the most important role in solid waste treatment and resource recycling industry.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".