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Record W2597748553

Literature Review on the Effects of Pollution on Respiratory Illnesses in Hamilton and its Cost on the Healthcare System.

2016· article· en· W2597748553 on OpenAlexaff
Nicole Rakowski

Bibliographic record

VenueGlobal Health: Annual Review · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCOPDHealth careEnvironmental healthMedicineHuman healthPulmonary diseasePollutionHealth problemsEnvironmental pollutionIntensive care medicinePsychiatryEnvironmental protectionEnvironmental scienceEconomicsEconomic growthBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Many scientific findings have shown that the effects of pollution on human health are enormous, including higher rates of lung disease, respiratory illnesses and chronic obstructive pulmonary disease (COPD). Hamilton will continue to suffer significantly if fossil fuel emissions are not reduced. This paper investigates how individuals’ health has been affected, and how that creates a burden for the health care system in Hamilton. Understanding the main contributors to pollution in Hamilton and possible solutions to improve air quality is of significant importance as it can lead to a better understanding on the Earth’s state in the future.

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)0.005
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: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.014
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.037
GPT teacher head0.358
Teacher spread0.321 · 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
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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