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Record W1976876340 · doi:10.1080/14634988.2011.575738

Dr. R. A. Vollenweider: the man and his science

2011· article· en· W1976876340 on OpenAlexaffabout
A El-Shaarawi

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsStatisticianScope (computer science)Trophic levelFriendshipSimplicityEnvironmental ethicsProcess (computing)SociologyOperations researchHistoryEcologySocial scienceEpistemologyComputer scienceMathematicsStatisticsPhilosophyBiology

Abstract

fetched live from OpenAlex

Richard Vollenweider was a great man and a brilliant scientist; his interests were broad and varied and he knew a number of fields rather deeply. All those who met his mind recognized that immediately. He was an internationalist, spoke eight languages and lived and worked in many countries. His basic field was biology, but he branched out in almost all aspects of water sciences and indeed was known as the lakes doctor. During the period 1968 to 1988, he worked at the Canada Centre for Inland Waters (CCIW) and the National Water Research Institute.My association with Richard started in 1973 when I joined CCIW as a research scientist statistician and continued until his passing on January 20, 2007. Richard had tremendous impact on my research through his advice, discussion and above all, his friendship. He understood the importance of statistics and quantitative sciences in understanding the scope of environmental problems and in providing practical solutions for their remediation. His ability to combine physical processes with statistical data analysis was responsible for the development of his simple but effective model for relating the trophic status of lakes to nutrient loadings, particularly phosphorous and nitrogen (Vollenweider et al., 1974). The simplicity of his model was the result of ignoring the internal dynamic process and concentrating on a dose-response, or input-output modelling process, that allowed for the prediction of the expected trophic state at a specified nutrient level. This played a major role in his fame and led to him receiving many awards including the Tyler Prize (equivalent to the Nobel Prize for aquatic science), the Naumann-Thienemann Medal of the International Society of Limnology, a Laureate of the UNEP Global 500 Roll of Honour, and a Fellow of the Royal Society of Canada. He received honorary doctoral degrees from the Universities McMaster, McGill, Uppsala and Ferrara.Dr. Vollenweider was a founding member of the International Environmetrics Society (TIES) and he gave the Keynote Address at its first meeting in Cairo in 1989, which was published in the first issue of the Environmetrics Journal (Vollenweider, 1990). He described Environmetrics as a crucial link of two scientific fields: statistics and environmental sciences. He specifically said when describing scientists applying statistics, “Most of us working on environmental issues, at one time or another, have used statistical techniques for analyzing data and have ventured into making inferences. The understanding of statistics for most of us however, is “second-hand,” i.e. learned from text books. This limits our ability to use statistics correctly. Indeed, rather than correct use, incorrect use, even misuse is often the rule, making statistics a questionable paraphernalia.” Then he went on to speak about statisticians, “There is also the counter-fact: statisticians, though highly qualified in their field, cannot develop and correctly apply their science without understanding the properties and functions of the dynamics of natural systems, as well as familiarity with the methods of analysis, are prerequisites for the correct application of new statistical concepts.” Those highlight Richard's philosophy of making empirical inferences based on data and scientific hypothesis. He was so pleased to be visiting Cairo during the conference because it reminded him of the days he spent working in Egypt and his love of Egyptian food and culture. In the last session of the conference meeting we were trying to identify a meeting place to hold the next conference. Richard suggested Como, Italy, and he played a crucial role in the organization of the next conference. So his support to TIES was a major help in the formation of that young society.Richard never stopped working after his retirement. I used to visit him regularly in his home to discuss his statistical modelling on the eutrophication of the Adriatic Sea. It was amazing to see this elderly gentleman working systematically on the analysis of a massive data set and getting so absorbed in the interpretation of the models’ features and parameters, particularly when new facts were revealed. I recall his last visit to CCIW when security phoned to inform that Richard had just arrived to see me. He came to my office and told me with a big smile on his face that he had just passed his driver's test and he decided to stop by and have a tour of the building. During the tour, he spoke of the programs and labs he designed, spoke with colleagues and associates and poignantly stood outside the library on the second floor where the plaque commemorating the Vollenweider lecture speakers is displayed. Two months later, he became ill and had to move to the hospital where I also visited him regularly.Overall Richard enriched his world and certainly my life, and for all of this I am thankful.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0220.018

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2011
Admission routes2
Has abstractyes

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