Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human bodies and minds evolved together—simultaneously and interdependently. Therefore, if nature provides for our physical health and well-being, it follows that nature also provides for our mental health and well-being. Psychologists have begun to recognize the impact that exposure to nature has on many aspects of our mental health and well-being; and a substantial body of supporting research and empirical data has accumulated. Nature’s beneficial effects on individuals’ mental health have been shown to extend beyond a mere restoration to baseline after negative periods of stress, anxiety, or depression. Nature’s beneficial effects extend to positively increasing true mental health and well-being, to elevating individuals beyond a neutral “just getting by” level and into an additive state of thriving and flourishing. This paper discusses highlights from the ever-increasing body of research findings and empirical data evidencing the positive and additive effects that nature has on our mental health and well-being. Included in this discussion are findings from a recent series of studies conducted at Grant MacEwan University that this author was involved in. The research summarized in this paper demonstrates that our relationship with nature is vital to our mental health and well-being.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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 it