Shifting terrain: Soil microbial communities in precarious climates and methodological contestations
Bibliographic record
Abstract
Soil microbial communities play critical roles as decomposers, plant pathogens, and plant mutualists in terrestrial ecosystems. Understanding how these communities will respond to a changing environment requires well-replicated studies across diverse ecological contexts and a careful assessment of the methodological dependencies of large-scale microbiological research. In my dissertation, I develop methods for the study of soil microbial communities across three registers: informatic, ecological, and anthropological. First, I present a software package to facilitate access to an open-access, continental-scale dataset of soil microbial DNA metabarcoding sequences for ecological research (Chapter 1). Second, I use this dataset to predict the spatial distributions of fungal taxa across the U.S. and Canada and quantify the sensitivity of soil fungal community composition to ongoing climate change (Chapter 2). Finally, taking an ethnographic approach, I observe how soilborne plant disease comes to be known through a variety of scientific practices that stabilize distinct objects of study (Chapter 3). Together, this work illustrates that while soil microbial communities everywhere may be shifting, these shifts are not the same everywhere. The North American boreal forest occupies a particularly precarious climate in which even slight warming can create major shifts in the composition of its soil fungal communities. Additionally, the enrollment of soil microbial communities in the transition to post-fumigant agriculture contributes to a complex political terrain wherein the possibilities for knowing soil microbial communities are entangled with the possibilities for managing soilborne plant disease. By showcasing diverse methods for studying soil microbial communities, this dissertation puts forth an ontological approach to scientific inquiry that does not presuppose objects as a given but rather enacts them anew through methodological innovations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".