Conference on Research and Innovations in Science and Technology of Material (CRISTMAS 2023)
Bibliographic record
Abstract
École Nationale Supérieure de Chimie de Paris, ParisTech, France 13-15 December 2023 This collection focuses on the recent innovations in Materials Science and advanced characterisations methods presented at the Conference on research and Innovations in Science and Technology CRISTMAS 2023. It covers topics ranging from advances in the most critical aspects in chemistry and material fabrication of nanomaterials, to the engineering of prototype devices and systems. Editorial Board: • Anna Baldycheva, University of Exeter, UK • Pavel Ginzburg, Tel Aviv University, Israel • Alexander Gumennik, Indiana University, USA • Andrei Gorodetsky, University of Birmingham, UK Scientific and Organising Committee: Anna Baldycheva, University of Exeter, UK Andrei Gorodetsky, University of Birmingham, UK Jèrome Tignon, Sorbonne University & Ècole Normale Superieure, France Pavel Ginzburg, Tel Aviv University, Israel Alexander Gumennik, Indiana University, USA Ben Hogan, Queen’s University, Canada Iveta Steblevska, Queen’s University, Canada Hani Bahrum, Tel Aviv University, Israel
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.119 | 0.051 |
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".