Bibliographic record
Abstract
On 4 March 2021, we lost a monumental figure in the world of aquatic sciences with the passing of David William Schindler.A mentor, colleague, friend, and inspiration to many, his scientific and environmental impacts were global in nature, but his was very much a Canadian story.The earlier Journal of the Fisheries Research Board of Canada and the present Canadian Journal of Fisheries and Aquatic Sciences (CJFAS) were the venues for more of his peer-reviewed publications than any other journal (64 of >300 papers).His strong appreciation for the intrinsic worth of nature made him a sentinel of freshwater ecosystems, advocating on their behalf for protection against the mounting pressures of humans.Besides being a natural historian, his keen intellect, sound logic, and candid manner enabled him to engage others, convincing many to change their ways even if it meant incurring inconveniences or financial loss.His environmental achievements were unprecedented, leading to international initiatives that helped improve and ensure air and water resources against the adverse impacts of global change.Like no other limnologist has or perhaps ever will, he advanced the aquatic sciences by filling critical knowledge gaps with ecologically realistic empirical evidence.As a child growing up near the Canadian-US border in northwestern Minnesota, his exploration of nearby lakes and forests lead to a lifelong connection for the outdoors.Later, higher education would foster this passion into a driven scientific curiosity about nature and eventually a need to advocate on its behalf.Undergraduate publications in Nature and Science and studies of trophic energy transfer as a Rhodes Scholar with Charles Elton at Oxford University set the stage for his career as a scientist.He would become an aquatic ecologist with a genuine and keen interest in the sciences, developing an expertise that span from biogeochemistry to biodiversity and ecosystem function.Despite offers of professorships from prestigious universities in the United States, he instead followed his love of "The North" and began his academic career in 1966 at a young Trent University in Peterborough, Ontario.Soon after, his path through life went near full circle as he returned to the Midwest just north of where he had grown up as a child.He had accepted a position at the Freshwater Institute in Winnipeg, taking on the challenge to set up the Experimental Lakes Area (ELA) and its experimental ecosystem-level approach in northwestern Ontario.At ELA, he began his proud association with the Journal of the Fisheries Research Board of Canada.He started by demonstrating his knack for technical creativity by reporting on inventions and techniques to quantify primary (Schindler and Holmgren 1971;Schindler et al. 1972) and secondary production (Schindler 1969; Schindler et al. 1971a).He also published several other papers at this time to establish the basis for valuable long-term data of numerous lakes in and around
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.017 | 0.035 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".