Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for Portuguese Journal of Public Health:Tânia Afonso, Lisbon, PortugalPedro Aguiar, Lisbon, PortugalVioleta Alarcão, Lisbon, PortugalRui Albuquerque, Lisbon, PortugalAlberto Alves, Maia, PortugalJoana Alves, Lisbon, PortugalLuísa Alves, Lisbon, PortugalAlberto J. Alves, Maia, PortugalRegina Ferreira Alves, Braga, PortugalFilipa Alves da Costa, Lisbon, PortugalEugénia Maria Garcia Jorge Anes, Bragança, PortugalCarla Araújo, Porto, PortugalMário Rui Araújo, Portalegre, PortugalLuisa Barros, Lisbon, PortugalDiana Bernardo, Santa Cruz, PortugalPaulo Boto, Lisbon, PortugalGisele Carvalho Câmara, Lisbon, PortugalCláudia Camilo, Lisbon, PortugalJosé Chen, Barcelona, SpainMario Coccia, Moncalieri, ItalyPaulo Jorge Marcos Cruchinho, Lisbon, PortugalRui Dang, Tashkent, UzbekistanMariane Helen de Oliveira, Chestnut Hill, MA, USAElizabeth Dean, Vancouver, BC, CanadaIsa Félix, Areeiro, PortugalAntonio Jorge Ferreira, Coimbra, PortugalAna Gloria Fonseca, Lisbon, PortugalMarise Reis Freitas, Natal, BrazilCarina Barros Freitas, Funchal, PortugalInês Fronteira, Lisbon, PortugalVânia Gaio, Lisbon, PortugalFátima Geada, Lisbon, PortugalAna Isabel Gomes, Lisbon, PortugalJosé Carlos Rodrigues Gomes, Leiria, PortugalJoana Gomes da Costa, Porto, PortugalManuel Gonçalves Pereira, Lisbon, PortugalMiguel Gouveia, Lisbon, PortugalBima Indra, Palembang, IndonesiaFrancisca Vargas Lopes, Rotterdam, The NetherlandsSilvia Lopes, Lisbon, PortugalHenrique Lopes, Lisbon, PortugalCarlota Louro, Lisbon, PortugalRita Luz, Lisbon, PortugalAna Rita Jesus Maria, Lisbon, PortugalMarta Moreira Marques, Lisbon, PortugalJorge Mendes, Lisbon, PortugalSónia Mendes, Lisbon, PortugalElias M.A. Militao, Lisbon, PortugalImmanuel Azaad Moonesar, Dubai, UAEAmanda Moore, London, UKAfonso Moreira, Lisbon, PortugalJoão Nascimento, Portalegre, PortugalDulce do Ó, Lisbon, PortugalCarlos Palos, Lisbon, PortugalCarlos A.O. Pavao, Atlanta, GA, USAJoão Pereira, Lisbon, PortugalCátia S. Pinto, Lisbon, PortugalAlexandra Marques Pinto, Lisbon, PortugalSónia Pós de Mina, Leiria, PortugalIsabel Maria Pires Sebastião Ramalhinho, Faro, PortugalJoana Ramos, Covilhã, PortugalVera Lúcia Raposo, Lisbon, PortugalRosane Valéria Viana Fonseca Rito, Niterói, BrazilJorge Olho Azul Rosário, Beja, PortugalArmando Brito de Sá, Lisbon, PortugalJoão Sá Gomes, Caparica, PortugalAna João Santos, Aveiro, PortugalAndrea Schmidt, Untermauerbach, AustriaFlorentino Serranheira, Lisbon, PortugalSusana Pereira Silva, Lisbon, PortugalFrederico Simões do Couto, Lisbon, PortugalAna Raquel Soares, Lisbon, PortugalM. Spiegel, Senftenberg, GermanySyafrawati Syafrawati, Padang Padang, IndonesiaAna Teles, Lisbon, PortugalFrans Verhey, Maastricht, The NetherlandsDaniel Virella, Lisbon, PortugalGuilherme Vitorino, Lisbon, Portugal
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.291 | 0.198 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".