Endoplasmic reticulum in health and disease: the 12th International Calreticulin Workshop, Delphi, Greece
Bibliographic record
Abstract
Abstract Starting from 1994, every 2 years, an international workshop is organized focused on calreticulin and other endoplasmic reticulum chaperones. In 2017, the workshop took place at Delphi Greece. Participants from North and South America, Europe, Asia and Australia presented their recent data and discussed them extensively with their colleagues. Presentations dealt with structural aspects of calreticulin and calnexin, the role of Ca2+ in cellular signalling and in autophagy, the endoplasmic reticulum stress and the unfolded protein response, the role of calreticulin in immune responses. Several presentations focused on the role of calreticulin and other ER chaperones in a variety of disease states, including haemophilia, obesity, diabetes, Sjogren's syndrome, Chagas diseases, multiple sclerosis, amyotrophic lateral sclerosis, neurological malignancies (especially glioblastoma), haematological malignancies (especially essential thrombocythemia and myelofibrosis), lung adenocarcinoma, renal pathology with emphasis in fibrosis and drug toxicity. In addition, the role of calreticulin and calnexin in growth and wound healing was discussed, as well as the possible use of extracellular calreticulin as a marker for certain diseases. It was agreed that the 13th International Calreticulin Workshop will be organized in 2019 in Montreal, Quebec, Canada.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".