Analysis of the Fifth Set of listing standards of China Science and Technology Innovation Board
Bibliographic record
Abstract
With the landing of micro-electrophysiology on the Science and Technology Innovation Board, the fifth set of Science and Technology Innovation Board listing rules appeared and hoped to promote the development of new things in domestic capital production. The fifth set of Science and Technology Innovation Board listing rules reflects the inclusiveness of the Science and Technology Innovation Board because this set of listing rules is for some unprofitable or small-profit research and development technology, innovation, and future development of enterprises listed standards. Including the core technology, production products and stage results, good market trends, and innovative technological advantages. Especially for most types of biological drug development or medical technology companies. Today, almost all the companies that use the fifth set of Science and Technology Innovation Board to go public have commercialized their products, gained operating income of 100 million yuan, and some companies have even earned profits. As time goes on, the fifth set of Science and Technology Innovation Board of the development is getting better and better. There are many successful cases for other companies with the same enterprises to reference, and the future trend is also on the rise. However, due to the special characteristics of the Technology Innovation Board, the liberalization of IPO listing also brings great risks to the auditing work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".