Notice bibliographique
Résumé
I first met Peter in the early 1970s.I knew of him mainly because he, like me, was a veterinarian who did research and published in international nonveterinary journals.Upon meeting him, which initially occurred when he passed through Saskatoon, Canada, where I had my laboratory in the early 1970s, it was soon apparent that neither of us were cut out to practice our intended craft of fixing diseased creatures.Instead, we were interested in viruses and figuring out how they interacted with their host.Nevertheless, both of us began our research careers working with real animals (i.e., those you eat or become fond of), but soon slipped to working with rodents.Over the years, I met Peter frequently, our families became and remain lifelong friends and I also got to know many, perhaps most, of Peter's trainees.These included some quite amazing characters such as Ralph Tripp and more sane folk such as Woody, Jack Bennink, Rhonda Cardin, Steve Turner, Mark Sangster, and many more.From the earliest years, Peter has been a role model for me.I always envied his common sense understanding of science and his ability to ask penetrating questions often criticizing people and their ideas without them realizing it.On the contrary, I always succeeded in insulting people even when I was trying to be nice!In the 1970s, I got to listen to many of Peter's talks and review some of his grants.Unlike the lucid Peter of today, his scientific stories were not the easiest to follow and his experimentation could be beyond the pale complex.Peter departed the Wistar where he spent several years solidifying his reputation as a viral immunologist and returned to Canberra to head up the Australian National University Department of Pathology.I joined him there for a minisabbatical in 1986.It was obvious that Peter enjoyed bench science but almost despised administration and other administrators (such as Bede Morris, also a veterinarian).He wanted to leave Canberra (''
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».