Editorial: Third peptide engineering meeting (PEM‐III) II. Peptide design and function
Bibliographic record
Abstract
This is the second of two special issues of Peptide Science featuring manuscripts based on lectures presented at the Third Peptide Engineering Meeting (PEM-III), held in Boston, Massachusetts, on July 17–18, 2003. PEM is an international workshop addressing structure-based approaches to peptide/protein interactions, and development of peptidic biomaterials, organized jointly by the American, European, and Japanese Peptide Societies. Representing the three Societies on the PEM-III Organizing Committee were: Charles M. Deber (Chair), Hospital for Sick Children & University of Toronto, APS; Claudio Toniolo, University of Padua, EPS; and Hisakazu Mihara, Tokyo Institute of Technology, JPS. The reader is referred to the Editorial in Peptide Science Vol. 76, #1, for further information on the PEM meetings. Themes discussed at PEM-III included peptides in medicine and disease; peptides as biomaterials and novel peptide structures; peptides in membranes; peptide assemblies; and peptides in molecular recognition. Manuscripts appearing in Peptide Science Vol. 76, #1 deal with subjects centered on “Peptides as Biomaterials.” In this issue, papers are collected around the topics of “Peptide Design and Function.”
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".