Bibliographic record
Abstract
Abstract In August 2016, the American Chemical Society (ACS) announced its plans to develop a chemistry preprint service, and that it was seeking collaborators as well as input from all stakeholders to ensure that the service met the needs of the chemical community [1]. A year later, nearly to the day, on 14 August 2017, the ACS and its collaborative partners, the Royal Society of Chemistry (RSC) and the German Chemical Society (GDCh), launched the beta version of ChemRxiv (pronounced ‘chem-archive’), as a non-profit, free service for chemists around the globe. All three partners are supplying financial support. But why chemistry and why now, more than a quarter of a century after the first preprint server, arXiv, was launched to serve the fields of physics, mathematics, astronomy, and computer science? (Coincidentally, arXiv was also launched on August 14th) [2]. To answer these questions, I spoke with Dr. Darla Henderson, ACS’ Assistant Director of Open Access Programs, who brought me up-to-date on the status of ChemRxiv as it approached its first birthday.
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.
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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.071 | 0.046 |
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".