Introduction to<i>Translation</i>
Bibliographic record
Abstract
We introduce here the inaugural issue of the new scientific journal Translation. The overarching aim of this endeavor is to establish a new forum for a broad spectrum of research in the area of protein synthesis in living systems ranging from structural biochemical, evolutionary and regulatory aspects of translation to the fundamental questions related to post-translational control of somatic phenomena in multicellular organisms including human behavior and health. The journal will publish high quality research articles, provide novel insights, ask provocative questions and discuss new hypothesis in this emerging field. Launching a new journal is always challenging. We hope that strong criteria for the peer-review process, transparency of the editorial policy and the scientific reputation of its founders, editors and editorial board assure the success of Translation; and we rely on continuing support of the scientific community in all aspects of the journal's activity.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.072 | 0.060 |
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".