Bibliographic record
Abstract
David Schindler's career, measured from the appearance of his first scientific paper in 1963, has spanned over 50 years.His accomplishments are remarkable for their diversity, their large-scale and long-term approaches to ecosystem problems, and for their extraordinary success at influencing environmental policy.Prof. Schindler has made such tremendous contributions to so many areas of aquatic environmental science that it is hard to capture the incredible influence that he has had on the way we view aquatic resources and how we manage them.Dr. Schindler received his doctorate from Oxford University, where he studied as a Rhodes Scholar.During his highly productive career, he headed the International Joint Commission's Expert Committee on Ecology and Geochemistry and the US Academy of Sciences' Committee on the Atmosphere and the Biosphere.Amongst many other commitments, he served as President of ASLO and as a Canadian National Representative to the International Limnological Society (SIL).He is the author of about 300 scientific publications.Amongst his remarkable list of awards is the first Stockholm Water Prize-a prize often considered to be equivalent to the "Nobel Prize" that is awarded annually to a person or organization that contributes broadly to the conservation and protection of water resources and to improved well-being of the planet's inhabitants and ecosystems.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.035 |
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".