Multidisciplinarity in Microbiome Research: A Challenge and Opportunity to Rethink Causation, Variability, and Scale
Bibliographic record
Abstract
This essay, written by a biologist, a microbial ecologist, a biological anthropologist, and an anthropologist-historian, examines tensions and translations in microbiome research on animals in the laboratory and field. The authors trace how research questions and findings in the laboratory are extrapolated into the field and vice versa, and the shifting evidentiary standards that these research settings require. Showing how complexities of microbiomes challenge traditional standards of causation, the authors contend that these challenges require new approaches to inferences used in ecology, anthropology, and history. As social scientists incorporate investigations of microbial life into their human studies, microbiome researchers venture into field settings to develop mechanistic understandings about the functions of complex microbial communities. These efforts generate new possibilities for cross-fertilizations and inference frameworks to interpret microbiome findings. Microbiome research should integrate multiple scales, levels of variability, and other disciplinary approaches to tackle questions spanning conditions from the laboratory to the field.
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 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".