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Record W1997669520 · doi:10.1890/0012-9623-96.1.45

A Trailing Paper Trail

2014· article· en· W1997669520 on OpenAlexaboutno aff
Will Wilson

Bibliographic record

VenueBulletin of the Ecological Society of America · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGratitudeMemorizationMathematics educationCarrState (computer science)MathematicsSociologyEcologyPsychologyBiology

Abstract

fetched live from OpenAlex

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.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0100.000

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 head0.017
GPT teacher head0.186
Teacher spread0.169 · 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
Published2014
Admission routes1
Has abstractyes

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