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Record W4396975592 · doi:10.3389/ijph.2024.1607320

Corrigendum: Time-Trends in Air Pollution Impact on Health in Italy, 1990–2019: An Analysis from the Global Burden of Disease Study 2019

2024· erratum· en· W4396975592 on OpenAlexaff
Sara Conti, Carla Fornari, Pietro Ferrara, Ippazio Cosimo Antonazzo, Fabiana Madotto, Eugenio Traini, Miriam Levi, Achille Cernigliaro, Benedetta Armocida, Nicola Luigi Bragazzi, Ennio Cadum, Michele Carugno, Giacomo Crotti, Silvia Deandrea, Paolo Angelo Cortesi, Davide Guido, Ivo Iavicoli, Sergio Iavicoli, Carlo La Vecchia, Paolo Lauriola, Paola Michelozzi, Salvatore Scondotto, Massimo Stafoggia, Francesco Saverio Violante, Cristiana Abbafati, Luciana Albano, Francesco Barone‐Adesi, Antonio Biondi, Cristina Bosetti, Danilo Buonsenso, Giulia Carreras, Giulio Castelpietra, Alberico L. Catapano, Maria Sofia Cattaruzza, Barbara Corso, Giovanni Damiani, Francesco Esposito, Silvano Gallus, Davide Golinelli, Simon I Hay, Gaetano Isola, Caterina Ledda, Stefania Mondello, Paolo Pedersini, Umberto Pensato, Norberto Perico, Giuseppe Remuzzi, Francesco Sanmarchi, Rocco Santoro, Biagio Simonetti, Brigid Unim, Marco Vacante, Massimiliano Veroux, Jorge Hugo Villafañe, Lorenzo Monasta, LG Mantovani

Bibliographic record

VenueInternational Journal of Public Health · 2024
Typeerratum
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsYork University
FundersBill and Melinda Gates Foundation
KeywordsAir pollutionBurden of diseasePublic healthEnvironmental healthDisease burdenPollutionDiseaseMedicineGlobal healthEnvironmental sciencePopulation

Abstract

fetched live from OpenAlex

[This corrects the article DOI: 10.3389/ijph.2023.1605959.].

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)0.029
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.070
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0700.033

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 head0.052
GPT teacher head0.402
Teacher spread0.350 · 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
GenreEditorial

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

Citations2
Published2024
Admission routes1
Has abstractyes

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