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Record W7123946560 · doi:10.5281/zenodo.18223721

D3.7 - Digital Planning and Design Tools for Climate Neutral Cities 2

2025· article· en· W7123946560 on OpenAlexaff
Peter Scheibstock, Felicitas Leithner, Jill Theobald, Sara Rossi, Aurelija Matuleviciute, Diana Kupper, Alexandros Gkatsikos, Mattia Federico Leone, Cristina Visconti, Giovanni Nocerino, Montse Martinez, David Suñer, Maria Adriana Cardoso, Rita Salgado Brito, Maria do Céu Almeida, Rita Ribeiro, Catarina Jorge, Hans Gehrels, Dirk Eilander, Giacomo Blanco, Erica Bruno, G. Melis, Kostas Kalaboukas, Michalis Bourbos, Louise Francis, Amanda Newton, Will Brown, Kristen MacAskill

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Sustainable Development
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsDeliverableTask (project management)Work (physics)Key (lock)Climate change

Abstract

fetched live from OpenAlex

This deliverable presents the technical and user requirements of each tool included within UP2030’s Task 3.3, alongside a detailed case study of each tool’s use in UP2030 and a general step-by-step guide. Each tool focuses on supporting enhanced approaches to climate neutrality. Included in this document are 16 tools from 11 technical partners.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0760.037

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 head0.058
GPT teacher head0.257
Teacher spread0.199 · 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.

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

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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