Notice bibliographique
Résumé
Although this series is called Paper Trails, it's been people that have most influenced me, and to whom I owe many debts of gratitude: my high school physics teacher, Gene Scribner, several college teachers, including my main math teacher, Ralph Carr, my Ph.D. advisor, Chester Vause, and postdoc advisors, Bill Laidlaw, Lawrence Harder, Ed McCauley, Kris Vasudevan, and Roger Nisbet. Many people taught me fascinating ideas and phenomena, and making it through each stressful academic step was only possible by each of these people in some way, an appreciation that grows in the rearview mirror. I grew up on a farm in central Minnesota, and was a first-generation college student at the nearest state school, St. Cloud State University. Early on I loved science, which was fun, unlike farm chores. Putting together math and physical phenomena, and actually deriving the numbers that came out of experiments amazed me. Theory was an obvious endpoint for me: I couldn't memorize things, I did pretty well with math and computers, and experiments were just a bit too much like farm chores. I earned a Ph.D. in theoretical physics at the University of Hawaii at Manoa, and turned towards ecology during an extended 8-year postdoctoral re-education in Canada and California, winning a faculty position in “interdisciplinary science” in the former Zoology Department at Duke University. It was an exciting time for a young scientist applying mathematical and computational approaches to answer questions regarding the mechanisms determining the abundance, distribution, and evolution of organisms. Presently I focus on urban environmental issues, and policy surrounding them. The constant has been learning new-to-me science, and, fortunately, there's a lot of interesting science in this world. My paper trail begins with two papers important to me as a graduate student and my statistical physics interests. The first was the Metropolis et al. (1953) work that grew out of the World War II Manhattan project and forms the basis of Bayesian statistics. That statistical physics algorithm made use of socalled “canonical ensembles” of system states at constant temperature, but another algorithm pioneered by Creutz (1983) employed a “microcanonical ensemble” of states with equal energy. These algorithms, especially the latter, let me simulate idealized systems, called Ising and Potts models, of interconnected “magnets” with two (or more) state values, +1 or −1. Arriving in Calgary for a postdoc with Bill Laidlaw, I modified the simulation for diffusive fluid flow through a lattice of interconnected “pores” with two states, 0-empty, or 1-filled, an approach outlined by Kadanoff (1985) as random walking particles. At some point I realized that several types of such simulated fluids could co-occur and react, giving either a model of chemical reactions or ecological interactions. A 1991 paper, with coauthors Andre de Roos and Ed McCauley (de Roos et al. 1991), began a long series of papers on spatial ecology involving many different models that linked deterministic partial differential equations and stochastic interactions of discrete individuals. During this time, a short interlude in geophysics (seismic image enhancement) relied on a paper by Rothman (1985) that tied together optimization of complex systems and the Metropolis algorithm within that discipline. A second source was a book by Goldberg (1989) that explained using “genetic algorithms” for optimization. Kris Vasudevan and I tied these two parts together (Wilson and Vasudevan 1991). A third topic in Calgary was floral evolution. It extended my budding interest in optimization but was really driven by the fascinating examples of floral form and function presented by Lawrence Harder rather than any foundational paper. His questions regarding strategies that male and female sides of flowers could employ to maximize matings presented not only a new way to look at plants, but started a series of theoretical and mathematical challenges (Harder and Wilson 1994). Ecological and evolutionary topics took deeper turns while working with Roger Nisbet at UC-Santa Barbara. His book with Bill Gurney (Nisbet and Gurney 1982) and the book by Murray (1989) provided so much background for a deep understanding of deterministic and stochastic ecological models. At that point I felt more like an ecologist equipped with an exciting blend of biological, mathematical, and computational challenges. I could be obsessed with questions: Why do mutualisms between organisms persist when cheaters abound? What features determine spatial variation in population densities? How does smart foraging affect animal grouping? What role does foraging play in the coexistence of species? Why are some plant species hermaphroditic while others have two genders? Why do many hermaphroditic plant species self-fertilize? I covered many of these topics in my 2000 book, Simulating Ecological and Evolutionary Systems in C. Another large factor in my work was the ecology center, NCEAS, in Santa Barbara, which fostered interactions during a sabbatical and participation in working groups. These collaborations extended all of my projects into new directions, again more by people than papers. A new paper trail arose from Hubbell's (2001) neutral theory for species interactions as a foundation for community dynamics. General disagreement with the conceptual foundation, certainly on my part, led to a sabbatical group that worked on a Lotka-Volterra community model and approximate solution (Wilson et al. 2003) chock full of predictions. The seeds of a new change arose when I earned tenure in 2001, the same year my one and only NSF grant ended. When that grant was awarded, funding rates sat around 30%, but had dropped to around 5% in the mid-2000s and have never recovered. I simply couldn't get funding for anything, and, despite publishing four to five papers a year, in 2006 came the message, “no grant, no promotion.” I combined this clarity on the importance of scientific discovery and the general changes in higher education with a by-then much deeper involvement in local open space, environmental, and policy issues. Along with expanding this involvement (including service on the Farm Board), I also moved in that direction academically by publishing a 2011 book on urban environmental conditions, Constructed Climates, that covers diverse topics with many foundational papers cited therein. Presently I have a stormwater science book in review, but on that topic one interesting paper is Booth (1991), which provides a nice, concise discussion of the issues.
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) | 0,001 | 0,001 |
| 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) | 0,010 | 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 ».