Sixth International AFM BioMed Conference on AFM in life sciences and medicine, December 13 to 17, 2014, San Diego, California
Bibliographic record
Abstract
California, and the Life Science Division of The French Alternative Energies and Atomic Energy Commission (CEA), France.The conference was held in the Meeting Room of the Sanford consortium for Regenerative Medicine on December 13 to 17, 2014, and was chaired by Prof Adam J. Engler.Founded in June 2006, after a first seminal Frenchspeaking conference held on the same topics in Nîmes in June 2004, the AFM BioMed Conference brings researchers and students around the world to discuss the latest scientific results of AFM in life science and medicine (Casuso et al., 2011;Parot et al., 2007).The AFM BioMed organized its first international meeting in Barcelona, Spain in spring 2007 (Pellequer et al., 2007) followed, every 18 months, by a meeting in Monterey, California, in fall 2008(Kumar et al., 2009) ; in Crveni otok (Red Island) near the Adriatic City of Rovinj, Croatia in spring 2010 (Svetlicic et al., 2011); in Paris in summer 2011 (Scheuring et al., 2012); and in Shanghai in spring 2013 (Hu et al., 2014).
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.151 | 0.074 |
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".