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Record W4417169366 · doi:10.1109/dsd67783.2025.00056

ShapeFuture - Technical Progress After Year 1

2025· article· en· W4417169366 on OpenAlexaff
Norbert Druml, Martin Gschwandtner, Mayeul Jeannin, Rainer Matischek, Edgars Lielāmurs, Maksis Celitans, Kaspars Ozols, Nurullah Demiralay, Besir Tayfur, Ismail Sinan Gulbas, Nadir Kucuk, Isa Kiyat, Yahya Nasolo, Jens Brandt, N. Pütz, Thomas Bartz-Beielstein, Jose Isola, Nikola Mandic, Francesca Flamigni, Alexander Kuehhas, Gianluca Brilli, Paolo Burgio, Giacomo Paolieri, Jorge Villagrá, José Antonio Sánchez, Jacopo Sini, M. Violante, Lorenzo Giraudi, Paolo Santero, Uwe Kölbel, Moritz Schaffenroth, Panu Sjövall, Jarno Vanne, Morten Larsen, Nergis Gizem Yilmaz, Ziya Uygar Yengin, George Dimitrakopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAutonomous Vehicle Technology and Safety
Canadian institutionsResverlogix (Canada)
Fundersnot available
KeywordsAutomationTechnical progressProcess (computing)SovereigntyWork in process

Abstract

fetched live from OpenAlex

ShapeFuture will drive innovation in fundamental Electronic Components and Systems (ECS) that are essential for robust, powerful, fail-operational and integrated perception, cognition, AI-enabled decision making, resilient automation and computing, as well as communications, for highly automated vehicles. The overarching vision of ShapeFuture is to bring ECS Innovation to the heart of Europe’s Mobility Transformation, thereby elevating sovereignty by perfecting programmable ECS solutions for intelligent, safe, connected, and highly automated vehicles. In this paper, we detail not only the vision and mission of the ShapeFuture project, but we also showcase the results achieved during the first year.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.663
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.002
GPT teacher head0.195
Teacher spread0.193 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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