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Record W4399280782 · doi:10.1016/j.lana.2024.100797

Addendum to “Dynamic measurements of geographical accessibility considering traffic congestion using open data: a cross-sectional assessment for haemodialysis services in Cali, Colombia” [The Lancet Regional Health – Americas 2024; VOLUME 34, 100722, Published: May 3, 2024]

2024· article· en· W4399280782 on OpenAlexaff
Luis Gabriel Cuervo, Carmen Juliana Villamizar, Lyda Osorio, Maria B. Ospina, Diana E Cuervo, Daniel Cuervo, María O Bula, Pablo Zapata, Nancy J. Owens, Janet Hatcher-Roberts, Edith Alejandra Martín, Felipe Piquero, Luis Fernando Pinilla, Eliana Martínez-Herrera, Ciro Jaramillo Molina

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsBruyèreUniversity of OttawaQueen's University
Fundersnot available
KeywordsAddendumTraffic volumeOpen dataVolume (thermodynamics)Cross-sectional studyTransport engineeringMedicineComputer scienceGeographyRegional scienceEngineeringPolitical scienceWorld Wide WebPathology

Abstract

fetched live from OpenAlex

The Laboratorio de Barrios Populares PopuLab at the Universidad del Valle in Cali, Colombia, is an innovative space dedicated to studying and applying ideas surrounding self-built neighbourhoods and urban development. Its primary goal is to bridge the gap between the aspirations of communities in popular neighbourhoods and the urban planning proposals from local governments. PopuLab has authorized the use of its video, 'Video participativo: movilidad comuna 18 Cali, Colombia'(16) to enhance this article with testimonies from users of health services. The video has close captioning with automatic multilingual translation.

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)0.002
metaresearch head score (Gemma)0.023
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: none
Teacher disagreement score0.123
Threshold uncertainty score0.411

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1230.022

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 head0.364
GPT teacher head0.465
Teacher spread0.101 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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