Human–environment interactions in population and ecosystem health
Bibliographic record
Abstract
As the global human population continues to grow, so too does our impact on the environment. The ingenuity with which our species has harnessed natural resources to fulfill our needs is dazzling. Even as we tighten our grip on the environment, however, the escalating extent of anthropogenic actions destabilizes long-standing ecological balances (1, 2). The dangers of mining, refining, and fossil fuel consumption now extend beyond occupational or proximate risks to global climate change (3). Among a plethora of environmental problems, extreme climate events are intensifying (4, 5). Storms, droughts, and floods cause direct destruction, but also have pervasive repercussions on food security, infectious disease transmission, and economic stability that take their toll for many years. For example, within weeks of the catastrophic wind and flood damage from the 2016 Hurricane Matthew in Haiti, there was a dramatic surge in cholera, among other devastating repercussions (6, 7). In a world where 1% of the population possesses 50% of the wealth (8), those worst affected by extreme climatic events and the aftermath are also the least able to rebound. Compounding the impact of natural disasters, our progressively more intimate interactions with fragmented environments (9) have given rise to an era of disease emergence and re-emergence at unprecedented rates, as exemplified by recent outbreaks of the Ebola and Zika viruses. Furthermore, globalization to an extent that includes the airline travel of over eight million people every day has enabled such disease outbreaks to disseminate rapidly and pose a threat far beyond their areas of origin (10). Addressing these challenges requires an understanding of coupled human–environment dynamics, whereby human activity modifies an environmental system (often detrimentally), and the resulting environmental repercussions then impact humans. In turn, these impacts can potentially spur a shift in human activity toward protection and restoration. … [↵][1]1To whom correspondence should be addressed. Email: alison.galvani{at}yale.edu. [1]: #xref-corresp-1-1
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".