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Record W2460992563 · doi:10.1002/bes2.1249

Unearthed! The Amazing Microbiome Exposed: The Established Researcher

2016· article· en· W2460992563 on OpenAlexaffabout
John N. Klironomos

Bibliographic record

VenueBulletin of the Ecological Society of America · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
Fundersnot available
KeywordsEcologyMicrobiomeBiologyMicrobial ecologySociology

Abstract

fetched live from OpenAlex

Most of us can identify a group of people that have influenced the direction of our careers and of our research programs. For me, a few individuals were pivotal at different stages of my research career. As an undergraduate student majoring in biology at Concordia University, my goal was to enter a medical profession rather than a career in ecology. That all changed in my final year when I was introduced to soil microbial ecology by Paul Widden. I was dazzled by the diversity of organisms that could be viewed under the microscope. I found it very interesting that we could observe only a small proportion of the organisms in the soil, for many of which we have no idea what they do. I was hooked. This was the beginning of my interest in research. With a thirst for better understanding the microbial world, I then joined Bryce Kendrick's laboratory as a graduate student to study mycology. My research focused on the ecology of mycorrhizal symbioses, but it was also a great environment for a broad training in mycology and an appreciation of the important roles of fungi as saprobes, pathogens, and mutualists. As a postdoc in Mike Allen's group in San Diego, I focused on the use and management of mycorrhizal fungi in landscape restoration and their responses to climate change. John Klironomos. Photo taken by Miranda Hart. My research up to this point was exciting, but there was one aspect to the work that I worried about: Most of the research was built on experiments with plants grown in mycorrhizal treatments and nonmycorrhizal (sterile) controls. Such an approach is powerful in evaluating the contributions of mycorrhizal fungi. However, it is also of limited relevance as the nonmycorrhizal state is not typically found for most plants in nature. Nonetheless, this was (and still is) a standard method for studying mycorrhizal symbioses by many research groups. In 1997, a big change happened in my approach to studying plant–microbe interactions. While attending the Mycological Society of America meeting in Montreal, I listened to Jim Bever talk about a simple but powerful framework on plant–soil feedback. He showed how his concept could be used to study plant and microbial effects and responses at the same time. The approach was particularly attractive because it integrated soil microbes into dynamical frameworks of plant populations, which was novel at that time. The associated methodology was simple experimentation and had no requirement for nonmycorrhizal controls. Jim and his colleagues published their discoveries in a seminal paper (Bever, J. D., K. M. Westover, and J. Antonovics. 1997. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. Journal of Ecology 85:561–573). Over the following decade, the framework by Jim and his colleagues led to an explosion of research studies on topics such as population and community dynamics, multitrophic interactions, determinants of rarity and invasiveness, climate change impacts, conservation biology, and landscape reclamation, among others. In reflecting on the contribution by Jim and his colleagues and the resulting impact on my research and the entire field, I believe there lies an important lesson. Many argue that advances in soil ecology are limited by available techniques. I certainly believe this; for example, our ability to better understand the identity and functioning of soil microbes has been vastly improved with recent advances in genomic techniques. However, conceptual/theoretical contributions may be just as influential, if not more so. Jim and his colleagues were able to stretch the field of soil ecology in a way that has resulted in more powerful questions, which are leading to research studies designed to provide more profound insight.

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)0.009
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.006
Scholarly communication0.0120.015
Open science0.0010.011
Research integrity0.0050.015
Insufficient payload (model declined to judge)0.0270.017

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 head0.211
Teacher spread0.194 · 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 designObservational
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
Published2016
Admission routes2
Has abstractyes

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