A Review of “Good Nature: Why Seeing, Smelling, Hearing and Touching Plants is Good for Your Health”
Bibliographic record
Abstract
Rarely does the book have a title that not only tells you the subject but also invites you to discover its contents. The book is “Good Nature: why seeing, smelling, hearing and touching plants is good for your health” by Pegasus Books New York. The author is not just another nature enthusiast who believes that plants are important to our well-being. Kathy Willis CBE is a paleoecologist Professor of Biodiversity in the Department of Biology, University of Oxford. She was for 5 years the Director of Science at the Royal Botanical Garden, Kew, United Kingdom. Her goal in Good Nature is to bring together the scientific literature that has an empirical basis for plants affecting our health. Of course, all of us know that eating plants, that is, vegetables, is important to our well-being. Good Nature assembles the empirical and medical basis for some of the effects on our almost metaphysical senses, like seeing, hearing, touching, and smelling. This is not an approach like taking an herbal pill or hanging some crystal around your neck. It is about finding whether there is consistent empirical evidence of the natural environment affecting our well-being and then what exactly the interaction is between us and the environment. The goal is to find the physical basis of our reactions, for example, brain activity, blood pressure, fMRI, heart rate, level of stress hormones, lymphocyte levels, microbiota. Good Nature says we need to take seriously these effects of nature on our well-being and then encourage research so we can understand exactly how nature affects our health. If we can have a firm understanding of this connection, there are many medical activities we might do besides just walking in the woods or fields and that we can create a better natural environment. This is a book that is not simply about ecosystem services but how our environment reaches into us for health and wellness.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".