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Record W2996083605 · doi:10.1093/pch/pxz157

Global climate change and health in Canadian children

2019· article· en· W2996083605 on OpenAlexaffabout
Irena Buka, Katherine M. Shea

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsCanadian Paediatric Society
Fundersnot available
KeywordsClimate changeHealth careEffects of global warmingEnvironmental resource managementBusinessEnvironmental planningEnvironmental healthGlobal warmingMedicineGeographyPolitical scienceEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Climate change is a reality. Numerous expert authorities warn of the critical need to undertake and adapt environmental efforts to protect human health. Climate change is accelerating, and countries in high latitudes, such as Canada, are experiencing climate change more directly and, for some end points, more dramatically than mid- and low-latitude countries. Children are vulnerable to climate change health effects, and physicians and other health care providers need to be ready to identify, manage, and prevent climate change-related health hazards. This practice point highlights specific, climate change-related threats to the health of children and youth, and provides resources for health care providers. Climate challenges and their health impacts on children are described, based on key Canadian reports and scientifically referenced information. Enhanced awareness of the immediate and longer-term health effects of climate change on children allows physicians and other health care providers to counsel families and practice more effectively.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0060.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.035
GPT teacher head0.301
Teacher spread0.266 · 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
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

Citations8
Published2019
Admission routes2
Has abstractyes

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