Bibliographic record
Abstract
We are pleased to present this special issue in celebration of the contributions of Professor William W. Taylor, and to highlight his notable achievements in teaching and mentorship, service, inland fishery management, and international engagement.Dr. Taylor is a University Distinguished Professor in Global Fisheries Systems at Michigan State University. He joined the Department of Fisheries and Wildlife in 1980; how he grew the dynamic and diverse faculty serves as a legacy of his leadership as chair (1992-2008) and acting dean of the College of Agriculture and Natural Resources (1999-2001). Dr. Taylor possesses many leadership qualities which cultivated a remarkable career highlighted by flourishing relationships and partnerships that will influence Great Lakes science and management for many generations to come.He has had an illustrious career in Great Lakes fisheries ecology, population dynamics, governance, and management, received numerous awards, published extensively in scientific literature and has co-edited eight books, including the two editions of the seminal reference on Great Lakes fishery policy and management. Throughout his career, Dr. Taylor has been active in the American Fisheries Society, proudly serving as president of the society, the Michigan Chapter, and the North Central Division at various time points in his career. He held a U.S. Presidential appointment as a U.S. Commissioner (alternate) for the Great Lakes Fishery Commission, a U.S. Secretary of the Interior appointment to the Sport Fishing and Boating Partnership Council (which he chaired for eight years), and a gubernatorial appointment to Michigan's Aquatic Nuisance Species Coordinating Council. Dr. Taylor also served as associate director of the Michigan Sea Grant College Program, and was instrumental in the implementation of the first ever conference on global inland fisheries hosted at the Food and Agriculture Organization of the United Nations in collaboration with Michigan State University.The success of the programs and partnerships he built is owed in part to his vision, but importantly, to his kind, nurturing, caring, loyal, and honest personality. Relationships are built on trust. Dr. Taylor cares deeply about his dogs, family, students, friends, and colleagues, not necessarily in that order and we all look to him to adjust our moral compasses and help us make the best decisions. We suspect that many of the contributors to this issue owe, in one way or another, their current position in life to his strong influence!The 21 vignettes within this issue showcase highlights from Dr. Taylor's life and career and relay a number of anecdotes and stories from family, friends, students, and colleagues that underscore his caring personality, his keen skills in negotiation and delegation, and unique approach to experiential teaching and learning. While no two individuals have had identical experiences and outcomes, Dr. Taylor's steadfast belief that anything is achievable, his guidance (even when you did not seek nor recognize you were being guided) and his collaborative leadership have shaped us all.We hope readers enjoy and are inspired by these brief insights into the life of our distinguished colleague, mentor, and friend and celebrate with us his innumerous accomplishments in environmental policy and management from local to global perspectives.Thank you, Bill!Keep your rod tip up and reel, reel, reel!
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.512 | 0.492 |
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".