<i>iMeta</i> Conference 2025: Creating high‐impact international journals
Bibliographic record
Abstract
Conference series, themed "Creating High-Impact International Journals," held at the Huangjiahu Campus of Hubei University of Chinese Medicine from August 23rd to 25th, 2025, and focused on frontier topics such as microbiology, medicine, traditional Chinese medicine, botany, and research career development. The event aimed to support the development of researchers and strengthen the impact of academic journals. Through invited reports, thematic seminars, and poster presentations, the conference highlighted hot topics including multi-omics technologies, microbe-host interactions, AI-assisted research, live biotherapeutic products, and the modernization of traditional Chinese medicine. The event demonstrated the innovative momentum of interdisciplinary integration and technological convergence, providing an international platform for academic exchange and laying a foundation for building an innovative scientific research ecosystem and enhancing the global influence of Chinese academic journals.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.158 | 0.051 |
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".