The Index Research on Technical Innovation Ability of the Coal Enterprise Based on SEM
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 coal industry is our country important energy production industry, its technical innovation ability has the important influence on the national overall technical level and the competitive power. But our country coal production and the applied technical are quite backward, the speed of technical innovation is quite slow, therefore, appraising the technical innovation ability level of the coal enterprise correctly to discover its superiority and the insufficiency has the important strategic and practical significance for promoting the construction of technical innovation ability of our country coal enterprise. This article takes the structural equation model as a foundation. It calculates 5 dimensions’ path coefficient and various items’ loading coefficient of technical innovation ability of the coal enterprise to obtain the model of technical innovation ability index. The paper gives the general reference value of technical innovation ability index of the coal enterprise in order to helping the coal enterprise to appraise its technical innovation ability and discovering its superiority and the insufficiency to propose the corresponding countermeasure and suggestion. Key words: Coal enterprise; Enterprise technical innovation ability; Enterprise technical innovation ability index; SEM
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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.088 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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