Bibliographic record
Abstract
David More is one of western Canada’s exceptional painters. Based in the rural hamlet of Benalto, near Red Deer Alberta, he is part of a generation of landscape artists who emerged in the 1970s to make beauty out of the ordinary and challenge the expected with bold acts of creation. Throughout his career, More has returned to the garden as a deeply functional yet ritualistic space of human endeavour. The garden is a place of shelter and sanctuary, of colour and fragrance, of order and wilderness. The garden is a private space, carefully tended and planted, observed en plein air or through the living-room window. The garden is a public space, a park where people gather to let their natures blossom. The garden is the world, the nature that sustains and surround us, the environment we all live within, and all have a responsibility to cultivate and tend. Greatest Garden is a celebration of David More’s engagement with the garden as a multifaceted subject. Featuring over fifty original artworks, this book encompasses a career spent in conversation with gardens in their many and varied forms. With lively brushwork, a keen sense of colour, and an aptitude for expressive drawing and varied composition, More has found the garden in expected and unexpected places. In Greatest Garden you are welcomed to walk its sunlit paths.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.036 |
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".