Autophagy
Bibliographic record
Abstract
Multidisciplinary approaches are increasingly being used to elucidate the role of autophagy in health and disease and to harness it for therapeutic purposes. The broad range of topics included in the program of the Vancouver Autophagy Symposium (VAS) 2013 illustrated this multidisciplinarity: structural biology of Atg proteins, mechanisms of selective autophagy, in silico drug design targeting ATG proteins, strategies for drug screening, autophagy-metabolism interplay, and therapeutic approaches to modulate autophagy. VAS 2013 took place at the British Columbia Cancer Research Centre, and was hosted by the CIHR Team in Investigating Autophagy Proteins as Molecular Targets for Cancer Treatment. The program was designed as a day of research exchanges, featuring two invited keynote speakers, internationally recognized for their groundbreaking contributions in autophagy, Dr Ana Maria Cuervo (Albert Einstein College of Medicine, Bronx, NY) and Dr Jayanta Debnath (University of California, San Francisco). By bringing together international and local experts in cell biology, drug discovery, and clinical translation, the symposium facilitated rich interdisciplinary discussions focused on multiple forms of autophagy and their regulation and modulation in the context of cancer.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.057 |
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".