Advancing Scientific Communication in Proteomics
Bibliographic record
Abstract
Recommendations C ommunicating complex scientific concepts effectively in papers, figures, and grant proposals is as crucial as actual research.Recognizing this need, the Human Proteome Organization (HUPO) Education and Training Committee (ETC) launched the "Stylish Academic Writing" webinar series.Featuring experts in proteomics and scientific communication, the series aimed to enhance researchers' writing and communication skills at all career stages.While each webinar delved into specific aspects of scientific communication, ranging from manuscript preparation and data visualization to grant writing and open data practices, several cross-cutting themes emerged that are vital for advancing both individual careers and the proteomics field as a whole.The webinar series was presented as five different seminars
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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 | 0.045 | 0.090 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.036 | 0.057 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".