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Record W3128473151 · doi:10.1016/s2542-5196(21)00005-x

Planetary health care: a framework for sustainable health systems

2021· letter· en· W3128473151 on OpenAlexaffabout
Andrea J. MacNeill, Forbes McGain, Jodi D. Sherman

Bibliographic record

VenueThe Lancet Planetary Health · 2021
Typeletter
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBusinessHealth careGreenhouse gasSustainabilityIncentiveNatural resource economicsEnvironmental resource managementEconomic growthEconomics

Abstract

fetched live from OpenAlex

In 2018, the Intergovernmental Panel on Climate Change announced that to restrict global temperature rise to 1·5°C, greenhouse gas emissions must decrease 45% by 2030 compared with 2010, and reach net zero by 2050.1 In 2020, the UK National Health Service (NHS) committed to achieving net zero greenhouse gas emissions by 2040.2 This precedent-setting decision by one of the world's largest health systems firmly positions the health sector at the vanguard of environmental sustainability and affirms the indivisibility of planetary health and health-care delivery.

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)0.043
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0100.056
Scholarly communication0.0190.021
Open science0.0070.028
Research integrity0.0160.014
Insufficient payload (model declined to judge)0.0190.005

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 head0.077
GPT teacher head0.330
Teacher spread0.253 · 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 designTheoretical or conceptual
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

Citations267
Published2021
Admission routes2
Has abstractyes

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