Bibliographic record
Abstract
Planting a few extra trees around your house could add years to your life, according to new research. An Adelaide-based professor has been involved in a major Canadian study of 250,000 people which found people who live in streets with lots of trees feel younger and healthier. The study compared people's real and perceived health outcomes with how many trees were in their street and how many parks were nearby. Professor Lyle Palmer told our reporter Natalie Whiting the study has major implications for urban planning. LYLE PALMER: In all societies in the world, including Australia, the biggest predictor of health and health outcomes in people is income, and so what we found was that if I had 10 more trees in my street than you, then that equates to me having a $10,000 a year higher income than you in terms of the effects on my perception of my health - so do I feel healthy, do I feel well. And we know that, you know, feeling well, having a positive attitude is itself a big determinant of how, you know, our risk of getting disease and then if we get disease how we deal with that disease and what the outcome is.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".