Evaluation of Low-Carbon Scientific and Technological Innovation-Economy-Environment of High Energy-Consuming Industries
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
The coordination of scientific and technological innovation with economy and environment is conducive to the sustainable development of high energy-consuming industries. Under the background of realizing the “carbon peak and neutrality” goal in China, this paper constructs the evaluation index system of scientific and technological innovation, economy, and environment of high energy-consuming industries. Based on the coupling coordination theory, this paper analyzes the coordinated development of scientific and technological innovation, economy, and environment of high energy-consuming industries from 2011 to 2019 and analyzes the factors restricting the coordinated development of the three systems. The results show that with the emphasis on scientific and technological innovation and ecological environment, the coordination degree of the complex system of scientific and technological innovation, economy, and environment of high energy-consuming industries is gradually increasing. R & D investment, the proportion of total industrial output value in GDP, and coal consumption per 10000 yuan of industrial output value are the main influencing factors of the coordination of the three systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.000 |
| 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