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Record W4390585354 · doi:10.3389/fpubh.2023.1349985

Editorial: Applications of geospatial information technologies and spatial statistics in health services research

2024· editorial· en· W4390585354 on OpenAlexaff
Chao Song, Xiuli Wang, Erjia Ge, Xun Shi, Jay Pan

Bibliographic record

VenueFrontiers in Public Health · 2024
Typeeditorial
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersSichuan Province Science and Technology Support ProgramHealth Commission of Sichuan ProvinceSichuan UniversityNational Natural Science Foundation of China
KeywordsGeospatial analysisData scienceSpatial analysisComputer scienceHealth informaticsData miningInformation retrievalGeographyMedicineRemote sensingPublic health

Abstract

fetched live from OpenAlex

EDITORIAL article Front. Public Health, 04 January 2024Sec. Health Economics Volume 11 - 2023 | https://doi.org/10.3389/fpubh.2023.1349985

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.346
Teacher spread0.333 · 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 teacher head, not a consensus.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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