MétaCan
Menu
Back to cohort
Record W4404104368 · doi:10.1016/j.uclim.2024.102165

Towards better understanding the urban environment and its interactions with regional climate change - The WCRP CORDEX Flagship Pilot Study URB-RCC

2024· article· en· W4404104368 on OpenAlexaff
Gaby S. Langendijk, Tomáš Halenka, Peter Hoffmann, Marianna Adinolfi, Aitor Aldama Campino, Olivier Asselin, Sophie Bastin, Benjamin Bechtel, Michal Belda, Angelina Bushenkova, Angelo Campanale, Kwok Pan Chun, Katiana Constantinidou, Erika Coppola, Matthias Demuzere, Quang‐Van Doan, Jason P. Evans, Hendrik Feldmann, Jesús Fernández, Lluís Fita, Panos Hadjinicolaou, Rafiq Hamdi, Marie Hundhausen, David Grawe, Frederico Johannsen, Josipa Milovac, Eleni Katragkou, Nour El Islam Kerroumi, Sven Kotlarski, Benjamin Le Roy, Aude Lemonsu, Chris Lennard, Mathew Lipson, Shailendra K. Mandal, Luís E. Muñoz Pabón, Vassileios Pavlidis, Joni‐Pekka Pietikäinen, Mario Raffa, Eloisa Raluy-López, Diana Rechid, Rui Ito, Jan-Peter Schulz, Pedro M. M. Soares, Yuya Takane, Claas Teichmann, Marcus Thatcher, Sara Top, Bert Van Schaeybroeck, Fuxing Wang, Jiacan Yuan

Bibliographic record

VenueUrban Climate · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsOuranos
FundersAgencia Estatal de InvestigaciónGrand Équipement National De Calcul IntensifCentre National de la Recherche ScientifiqueConsejo Nacional de Investigaciones Científicas y TécnicasBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftUniversity of the West of EnglandHORIZON EUROPE Framework ProgrammeFonds Wetenschappelijk OnderzoekDeutsches Zentrum für Luft- und RaumfahrtUniverzita Karlova v Praze
KeywordsClimate changeClimatologyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

G.S. Langendijk et al.

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.095
GPT teacher head0.267
Teacher spread0.172 · 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 designObservational
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

Citations19
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueUrban ClimateSame topicUrban Heat Island MitigationFrench-language works237,207