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Toward Measuring the Impact of Ecological Disintegrity on Human Health

2001· article· en· W2013632047 on OpenAlexaff
Lee E. Sieswerda, Colin L. Soskolne, S C Newman, Donald Schopflocher, Karen E. Smoyer

Bibliographic record

VenueEpidemiology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsAlberta HealthUniversity of Alberta
FundersWorld Resources InstituteWorld Bank Group
KeywordsLife expectancyPer capitaSocioeconomic statusEcological healthGross domestic productEcological studyConfoundingEcologyPopulationEcological indicatorGeographyEnvironmental healthDemographyEconomicsBiologyMedicineEconomic growthEcosystem

Abstract

fetched live from OpenAlex

Ecological integrity refers to the ability of environmental life-support systems to sustain themselves in the face of human-induced impacts. We used a correlational, aggregate-data study design to explore whether life expectancy, as a general measure of population health, is linked to large-scale declines in ecological integrity. Most of the data were obtained from World Resources Institute publications. Selected surrogate measures of ecological integrity and gross domestic product (GDP) per capita (as a socioeconomic confounder) were modeled, for the first time, using linear regression techniques with life expectancy as the health outcome. We found a modest relation between ecological integrity and life expectancy, but the direction of the association was inconsistent. When GDP per capita was controlled, the relation between ecological integrity and life expectancy was lost. GDP per capita was the overwhelming predictor of health. Any relation between ecological integrity and health may be mediated by socioeconomic factors. The effect of declines in ecological integrity may be cushioned by the exploitation of ecological capital, preventing a direct association between measures of exposure and outcome. In addition, life expectancy may be too insensitive a measure of health impacts related to ecological decline, and more sensitive measures may need to be developed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.473
GPT teacher head0.480
Teacher spread0.008 · 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 teacher head, not a consensus.

Study designObservational
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

Citations29
Published2001
Admission routes1
Has abstractyes

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