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Enregistrement W1976876340 · doi:10.1080/14634988.2011.575738

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

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

Notice bibliographique

RevueAquatic Ecosystem Health & Management · 2011
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueSoil and Water Nutrient Dynamics
Établissements canadiensEnvironment and Climate Change Canada
Organismes subventionnairesnon disponible
Mots-clésStatisticianScope (computer science)Trophic levelFriendshipSimplicityEnvironmental ethicsProcess (computing)SociologyOperations researchHistoryEcologySocial scienceEpistemologyComputer scienceMathematicsStatisticsPhilosophyBiology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,075

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,014
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0050,004
Science ouverte0,0010,002
Intégrité de la recherche0,0040,010
Charge utile insuffisante (le modèle a refusé de juger)0,0220,018

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,232
Écart entre enseignants0,211 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission2
Résumé présentoui

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