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Record W4226170070 · doi:10.5864/d2022-005

Editor’s Message

2022· article· en· W4226170070 on OpenAlexvenueno aff
Andrew Papadopoulos

Bibliographic record

VenueEnvironmental Health Review · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Many of us have been fully absorbed in COVID-related activities over the past 2 years, and now as some of these activities are being lessened, many eyes will be turned to the pandemic response and toward the public health system going forward.Work and discussions are already taking place in many provinces across Canada as well as at the federal level.I am quite sure many of us have experienced challenges during this pandemic response and have had a difficult time keeping up with activities that did not involve COVID-19.This journal is an ideal forum to share lessons learned during the COVID-19 pandemic response as well as to turn an eye toward the future of environmental public health.There were many areas where environmental public health professionals were extended and where they previously may not have participated.Further, there were areas ignored that now must be revisited.The main function of the Environmental Health Review is to increase the knowledge of and to disseminate information to environmental public health professionals across Canada.This role has never been more important.

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)0.011
metaresearch head score (Gemma)0.067
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 score0.048
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.067
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0100.004
Open science0.0040.003
Research integrity0.0420.031
Insufficient payload (model declined to judge)0.0480.029

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 head0.035
GPT teacher head0.240
Teacher spread0.205 · 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

Citations0
Published2022
Admission routes1
Has abstractno

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