40th International Asilomar Chromatin, Chromosomes, and Epigenetics Conference
Bibliographic record
Abstract
The 40th International Asilomar Chromatin, Chromosomes, and Epigenetics Conference was held in the Asilomar Conference Grounds, Pacific Grove, California, USA, on 6-9 December 2018. The organizing committee consisted of established scientists in the fields of chromatin and epigenetics: Sally Pasion and Michael Goldman from the Biology Department, San Francisco State University, California, USA; Philippe Georgel from the Department of Biological Sciences, Marshal University, West Virginia, USA; Juan Ausió from the Department of Biochemistry and Microbiology, University of Victoria, British Columbia, Canada; and Christopher Eskiw from the Department of Biochemistry, University of Saskatchewan, Saskatchewan, Canada. The meeting had two keynote speakers: Jessica Tyler and Jennifer Mitchell, and it covered topics on transcription, replication and repair, epigenetics, cell differentiation and disease, telomeres, and centromeres and it had two sessions devoted to nuclear and genomic organization. It encompassed the enthusiastic presentations of excellent trainees within the breathtaking natural setting of Pacific Grove.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.025 |
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".