IProPBio Workshop Series - ProGreS 3rd WORKSHOP From biomass characterization to process synthesis
Bibliographic record
Abstract
The Ipropbio workshop series comes to its third stop at Salamanca. ProGres III brings you to the ancient roman city of Helmantica, with its roman bridge, its two cathedrals and the 3rd oldest University in Europe, established in 1218. Although the ancient says goes “Quod natura non dat, Salmantica non præstat”, what Nature does not provide, you are not going to get in Salamanca, referring to those students sent to study at the University, we certainly hope your visit to Salamanca will bring you knowledge and an enjoyable experience The Organising committee of the 3rd ProGreS workshop is pleased to offer you a remarkable scientific programme giving the opportunity to open fruitful scientific and technological discussion about relevant challenges in achieving the goals of a more sustainable society. We acknowledge the special contribution as a plenary lecture of Professor Ignacio E. Grossmann (CMU) as well the contributions of the members of the IProPBio consortium. Moreover, we are grateful for the participation of some of the major experts in Biorefineries analysis and design around Spain. All participants and contributions will open new horizons and improve the final outcome of this meeting. The Organizing Committee gratefully acknowledges all authors for their contributions and all sponsors for their financial support. Welcome and enjoy the science of the multidisciplinary workshop and the beauty of the city from 3-4 November at Colegio Arzobispo Fonseca. We wish you an excellent conference both scientifically and socially, and an enjoyable stay in Salamanca.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.044 |
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".