Bibliographic record
Abstract
An environmentalist, a geneticist, a broadcaster and an all around conservationist, Dr. David Suzuki is a Canadian born “lover of the land”, directing the world’s attention to the environment and the damage that human life has bestowed on it. In his youth, Suzuki faced much racial discrimination as a Japanese Canadian born during the Second World War. He quickly grew fondness of nature and biology, spending most of his early life outdoors learning from nature. His professional career began as an Assistant Professor in Genetics at the University of Alberta, where it was quickly evident that he was an outstanding speaker. His speaking ability, combined with his love of nature, landed him many broadcasting positions; his most notable being his time spent hosting “The Nature of Things”. Aside from his many awards, one of Suzuki’s greatest achievements was his creation of the David Suzuki Foundation, a tool through which people could learn about and address environmental issues, a combined idea of Suzuki and his wife Tera Cullis. The foundation has not only provided challenges for individuals to make a difference, but has also government and industry leaders, inspiring people everywhere. “I have no Illusions that I am so important that I can turn everything around, I have no illusions that my foundation is going to make a huge difference, but I believe if millions of people like me and thousands of organizations like mine are all doing their small part we can become an irresistible force” – David Suzuki.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".