Bibliographic record
Abstract
All I hope this finds you safe and well and learning to adapt to the "new normal".2020 has been a tumultuous year for all of us with all scientific meetings, including our own ARTERY 20, either cancelled or held virtually online, giving us little or no opportunity for personal interaction and scientific networking.I have therefore commissioned a special feature for the current issue of Artery Research "Letter from".For this feature I have asked members of ARTERY and our supporters to write short letters from wherever they are around the world.In these letters the authors give short updates as to how the COVID-19 pandemic has affected them personally in their respective cities and countries worldwide.I am delighted to report that the response has been very positive and I hope very much that you will enjoy reading them and it will serve, in some small way, to bring us together during these difficult times.I hope that many of you will be able to take part in what will be a virtual online meeting of ARTERY20 in October, with final details to be announced shortly.Finally, wherever you are, stay safe and well and I look forward to being able to see many of you in person, if socially distanced, before too much longer.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".