Nature Nurtures: Investigating the Potential of School Grounds Toyota Canada Inc. and its Dealerships – Proudly supporting outdoor classrooms in Canadian schools. Bringing Nature to our Cities Bringing Nature to our Cities
Bibliographic record
Abstract
nurturing as possible. The Toyota Evergreen Learning Grounds Program represents a commitment to contribute positively to the health and well-being of future generations by educating children about the importance of restoring and preserving the environment. Teachers, students and community members are invited to participate in a nation-wide effort to reclaim Canada’s school grounds and to create healthy learning environments. Toyota Evergreen Learning Grounds Charter The Evergreen and Toyota Canada Inc. partnership represents a shared commitment to positively contribute to the school grounds, environment and emotional and physical development of Canada’s children. We believe that the provision of educational resources and the support of caring citizens will transform school grounds into healthier, more dynamic places for learning. We believe that by combining Toyota’s commitment to corporate social responsibility with Evergreen’s ecological restoration practices we will enhance our combined reach and the quality of business, community and learning. We commit our organizations to lead by example, and to provide measurable and meaningful resources and support to Canada’s schools and to the communities in which we work. It is our sincere intent to foster a new spirit of community involvement and environmental stewardship within the hearts and minds of Canada’s future: children and youth.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".