Bibliographic record
Abstract
Colleagues, The program for the sixth Peptide Engineering Meeting, PEM6, is shaping up nicely. All of the invited speakers have confirmed their participation and we are currently collecting abstracts from which short talks will be selected. The meeting will be held October 2nd through the 5th at Emory University Conference Center. This international workshop will address structure-based approaches to peptide design, peptide-protein interactions, peptide-membrane interactions, peptide bioactivity, and peptide-based materials. The theme of this years meeting is Learning from Nature to Engineer Peptides. Understanding the conformational principles of peptide and protein folding. Uncovering the physical underpinnings of peptide and protein self-association and its role in evolved physiological function and in disease-related aggregation. Mimicking Nature with novel amino acid and peptide chemistry. Engineering peptides based on the fundamental biophysical principles outlined by Nature for targeted applications in peptide-based therapy, cell and tissue targeting, drug delivery, modulators of cell phenotype and the design of novel materials, amongst others. This year's meeting begins with an opening keynote address by Laura Kiessling, from the University of Wisconsin and ends with a closing address by Padmanabhan Balaram, Director of the Indian Institute of Science, Bangalore. In addition, lectures will be given by Annelise Barron (USA), Vincent Conticello (USA), Bill DeGrado (USA), Gilles Guichard (France), Sihyun Ham (Korea), Robert Hancock (Canada), Ada Kapurnioto (Germany), Chie Kojima (Japan), Beate Koksch (Germany), Takaki Koide (Japan), Hee-Seung Lee (Korea), Junko Ohkanda (Japan), Annalisa Pastore (England), Sheena Radford (England), Hanna Rapaport (Israel), Seiji Sakamoto (Japan), Joan Shea (USA), Margaret Sunde (Australia), Meritxell Teixido (Spain), and Bing Xu (USA). We are delighted that this meeting will have an honorary guest, Dr. Isabella Karle, whose long career in peptide science has led to seminal contributions. We will be saluting her 90th birthday! Please visit the website at (http://www.umass.edu/pem6) for more information. I look forward to welcoming all of you to Atlanta.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.038 |
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".