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Record W4280508618 · doi:10.1038/s41598-022-11769-6

Author Correction: Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

2022· erratum· en· W4280508618 on OpenAlexaff
Malcolm Mistry, Rochelle Schneider, Pierre Masselot, Dominic Royé, Ben Armstrong, Jan Kyselý, Hans Orru, Francesco Sera, Shilu Tong, Éric Lavigne, Aleš Urban, Joana Madureira, David García-León, Dolores Ibarreta, Juan-Carlos Ciscar, Luc Feyen, Evan de Schrijver, Micheline de Sousa Zanotti Stagliorio Coêlho, Mathilde Pascal, Aurelio Tobı́as, Barrak Alahmad, Rosana Abrutzky, Paulo Hilário Nascimento Saldiva, Patricia Matus Correa, Nicolás Valdés Orteg, Haidong Kan, Samuel Osorio, Ene Indermitte, Jouni J. K. Jaakkola, Niilo Ryti, Alexandra Schneider, Veronika Huber, Klea Katsouyanni, Antonis Analitis, Alireza Entezari, Fatemeh Mayvaneh, Paola Michelozzi, Francesca de’Donato, Masahiro Hashizume, Yoonhee Kim, Magali Hurtado‐Díaz, César De la Cruz Valencia, Ala Overcenco, Danny Houthuijs, Caroline Ameling, Shilpa Rao, Xerxes Seposo, Baltazar Nunes, Iulian‐Horia Holobâcă, Ho Kim, Whanhee Lee, Carmen Íñiguez, Bertil Forsberg, Christofer Åström, Martina S. Ragettli, Yue Leon Guo, Bing‐Yu Chen, Valentina Colistro, Antonella Zanobetti, Joel Schwartz, Trần Ngọc Đăng, Do Van Dung, Yuming Guo, Ana María Vicedo-Cabrera, Antonio Gasparrini

Bibliographic record

VenueScientific Reports · 2022
Typeerratum
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of OttawaHealth Canada
Fundersnot available
KeywordsClimatologyEnvironmental scienceMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

The original version of this Article contained an error in Affiliation 25, which was incorrectly given as ‘Faculty of Medicine ArqFuturo INSPER, University of São Paulo, São Paulo, Brazil’. The correct affiliation is listed below.

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)0.004
metaresearch head score (Gemma)0.082
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.082
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0680.032

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 head0.130
GPT teacher head0.381
Teacher spread0.250 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueScientific Reports→Same topicClimate Change and Health Impacts→French-language works237,207→