David W. Schindler (1940–2021): Trailblazing scientist and advocate for the environment
Bibliographic record
Abstract
On March 4th, 2021, the global aquatic sciences community lost one of its most influential scientists, David W. Schindler. Dave’s landmark research that led to better protection of fresh waters around the world, his uncanny ability to identify, raise the profile of, and address key crises in aquatic sciences, and his tireless education of the public and decision makers on environmental issues have left an unmatched legacy. David W. Schindler. Image credit: John Ulan (University of Alberta, Edmonton, AB, Canada). Throughout his monumental career, Dave’s research shone a light on the ecological crises unfolding in freshwater ecosystems. His trailblazing approach included listening to those who were closest to the environment or a problem he was working on, particularly the wisdom of Indigenous knowledge holders, applying science in a way that was respectful of Indigenous ways of knowing, and using research findings and his own reputation to amplify their voices and effect more holistic stewardship. Much to the chagrin of some politicians and industries, Dave’s remarkable scientific acumen was matched by his tireless commitment and formidable ability to raise public awareness of environmental issues. For him, fresh waters had to be protected, and to do so, science had to be communicated: it was this moral conscience and modus operandi that underpinned Dave’s decades of effecting real-world change. For many years, he was the most quoted Canadian academic in the media, a measure of his unwavering commitment to putting science in the public eye and one that was recognized with the Royal Canadian Institute’s Sandford Fleming Medal for communication of science. Dave “always believed that a scientist can be an advocate,” and he practiced what he preached. Dave grew up on a farm in northern Minnesota, spending his formative years working with his hands and stomping around the forests and lakes, while … [↵][1]1To whom correspondence may be addressed. Email: karenkidd{at}mcmaster.ca. [1]: #xref-corresp-1-1
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.026 |
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".