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Record W4379548550 · doi:10.5281/zenodo.8012140

The Significance of Accelerated Discovery of Advanced Materials to address Societal Challenges

2023· preprint· en· W4379548550 on OpenAlexaff
Simon Stier, Christoph Kreisbeck, H. Ihssen, Matthias Albert Popp, Jens Hauch, Kourosh Malek, Marine Reynaud, Johan M. Carlsson, Lukas Gold, Fedor P.M. Goumans, Ilian T. Todorov, Andreas Räder, Shahbaz Tareq Bandesha, Wolfgang Wenzel, Philippe Jacques, Oier Arcelus, Francisco García‐Moreno, Pascal Friederich, Mario Maglione, Simon Clark, Anssi Laukkanen, Montserrat Casas Cabanas, Javier Carrasco, Ivano E. Castelli, Helge S. Stein, Tejs Vegge, Sawako Nakamae, M. Fabrizio, Mark Kozdras

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2023
Typepreprint
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsData scienceComputer science

Abstract

fetched live from OpenAlex

Societal Challenges demand for Advanced Materials, which in turn promise economical potential. Material Acceleration Platforms (MAPs) will decrease their development time and cost. We comment on implications for science, industry and policy concluding with necessary steps towards establishment of MAPs.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.992
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0040.011
Open science0.0020.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0150.003

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.

Opus teacher head0.102
GPT teacher head0.358
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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