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Record W3096166997 · doi:10.1002/fsh.10551

In Memoriam

2020· article· en· W3096166997 on OpenAlexaboutno aff
Dick Beamish

Bibliographic record

VenueFisheries · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsWifeGovernment (linguistics)Fish <Actinopterygii>ManagementFisheryLibrary scienceSociologyPolitical scienceLawPhilosophyComputer science

Abstract

fetched live from OpenAlex

Donald James Noakes Many readers will know Don Noakes as the founding editor of Marine and Coastal Fisheries. Our fisheries science and management community lost Don on October 19, 2020 from complications associated with spinal cancer. He was only 65 years old and just retired in 2019. Don was proud to have graduated from the University of Waterloo as an engineer with expertise in applied time series modelling. He was equally proud to have been a student of Keith Hipel for both his master’s and doctoral degrees. Don arrived at the Pacific Biological Station immediately after receiving his PhD in 1985 and quickly became involved in a range of activities leading up to positions as head of aquaculture, head of Pacific salmon research, and then director from 1995 to 2003. Don left the Canadian federal government in 2003 to become dean of the School of Advanced Technologies and Mathematics as well as Associate Vice President of Research and Graduate Studies at Thompson Rivers University in Kamloops, British Columbia. After 11 years, he and his wife Olga wanted to return to Nanaimo, British Columbia, and in October 2014, he accepted the position as the dean of the Faculty of Science and Technology at Vancouver University in Nanaimo. Don Noakes had a diversity of talents with a solid background in applied mathematics. He was always available to help his colleagues with their analytical issues. He was an expert on the management of Pacific fisheries in general and Pacific salmon Oncorhynchus spp. and shellfish in particular, as identified by his publications. It was his analyses that helped convince colleagues, often for the first time, that climate changes and resulting impacts on ocean survival had become the major factors affecting Pacific salmon production. However, some of his most important contributions came from his research and advice to government and industry for the sustainable development of aquaculture on the Pacific coast of Canada. His skill in social science as well as a comprehensive analytical understanding of scientific issues made him a sought after communicator of science to the aquaculture industry and senior government officials. The aquaculture industry considered him a kind face at research meetings and workshops because of his thoughtfulness, his focus on evidence-based decision making, and his dry sense of humor. His last publication may be his most important. “Oceans of Opportunity: a Review of Canadian Aquaculture” was published in 2018 in Marine Economics and Management, volume 1, issue 1. In the paper, Don identifies the opportunities and challenges that are needed to use the ocean to produce seafood and provide employment, particularly in more remote locations along the West Coast. Some day there will be major coastal seafood farming industries stretching from Mexico to Canada to Alaska. It will be recognized that few scientists have contributed as much to the development of the marine aquaculture industry as Don Noakes. Don Noakes was a steady hand at the tiller with a wealth of insight and the odd pun. He was also a golfer, curler, photographer, gardener, barbecuer, candy maker, and his recent passion was the bagpipes. He was a proud member of the Kamloops Pipe Band, the Pacific Gael Pipe Band, and a supporter of all things Scottish. Most of all, Don Noakes was a true friend. Don will be remembered at a private family celebration and a larger gathering when pandemic restrictions are over. We send our condolences to his wife, Olga, his family, friends, and colleagues.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0820.001

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.

Opus teacher head0.024
GPT teacher head0.231
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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