Bibliographic record
Abstract
Abstract Human beings are part of natural ecosystems and depend on them for their survival. In a rapidly changing environment and with increasing urbanization, this dependence is challenged. Natural environments affect human health and well-being both directly and indirectly. Urban green and blue areas provide opportunities for stress recovery and physical activity. They offer spaces for social interactions in the neighborhood and places for children’s play. Chronic stress, physical inactivity, and lack of social cohesion are three major risk factors for noncommunicable diseases, and therefore abundant urban greenery is an important asset for health promotion. Through numerous ecosystem services natural environments play a fundamental role in protecting health. Various populations depend on nature for basic material, such as fresh water, wood, fuel, and nutritious food. Biodiverse natural areas are also necessary for regulating the environment and for mitigating and adapting to climate change. For example, tree canopy cover can reduce the urban heat island effect substantially, preventing excess morbidity during heat waves. This natural heat-reducing effect also lessens the need for air conditioning systems and as a consequence decreases energy spending. Urban trees also support storm-water management, preventing flooding and related health issues. Air pollution is a major threat to population health. Urban trees sequester pollutants and, even though the effect may be relatively small, given the severity of the problem it may still have some public-health implications. The evidence around the effects of natural environments on health and well-being is steadily increasing. Several pathways and mechanisms are suggested, such as health services through functional ecosystems, early life exposure to biodiverse microbiota, which is important for the immune-system development, and sensory exposure, which has direct neurobiological impact supporting cognitive development and stress resilience. Support for several pathways is at hand that shows lower mortality rates and prevalence of cardiovascular and respiratory diseases, healthier pregnancy outcomes, reduced health inequalities, and improved mental health in urban areas with greater amounts of green and blue space. Altogether, the interactions between healthy natural environments and healthy people are multiple and complex, and require interdisciplinary attention and action for full understanding and resilient development of both nature and human beings.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".