Toward Measuring the Impact of Ecological Disintegrity on Human Health
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".