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Record W2995809825 · doi:10.1016/s2542-5196(19)30246-3

The Paris agreement: charting a low-emissions path for a child born today

2019· article· en· W2995809825 on OpenAlexaff
Courtney Howard, Yassen Tcholakov, Christian Holz

Bibliographic record

VenueThe Lancet Planetary Health · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsMcGill UniversityUniversity of Calgary
Fundersnot available
KeywordsScopusClimate changePolitical scienceMEDLINELaw

Abstract

fetched live from OpenAlex

10 years have passed since The Lancet first stated that “Climate change is the biggest global health threat of the 21st century”,1 and almost 5 years ago, the 2015 Lancet Commission on Health and Climate Change concluded that tackling this threat could be the greatest health opportunity of this time.2 Since then, initiatives such as the Planetary Health Clinicians' Initiative, fossil fuel divestment by health-related groups,3 and declarations of a climate emergency by health actors are evidence that the world's medics are mounting ever-greater responses to this existential threat.

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)0.020
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0070.009
Scholarly communication0.0140.016
Open science0.0040.017
Research integrity0.0210.022
Insufficient payload (model declined to judge)0.0340.013

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 head0.041
GPT teacher head0.301
Teacher spread0.260 · 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 designSimulation or modeling
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

Citations5
Published2019
Admission routes1
Has abstractyes

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