Back to the Future of Soil Metagenomics
Bibliographic record
Abstract
Direct extraction and characterization of microbial community DNA through PCR amplicon surveys and metagenomics has revolutionized the study of environmental microbiology and microbial ecology. In particular, metagenomic analysis of nucleic acids provides direct access to the genomes of the "uncultivated majority." Accelerated by advances in sequencing technology, microbiologists have discovered more novel phyla, classes, genera, and genes from microorganisms in the first decade and a half of the twenty-first century than since these "many very little living animalcules" were first discovered by van Leeuwenhoek (Table 1). The unsurpassed diversity of soils promises continued exploration of a range of industrial, agricultural, and environmental functions. The ability to explore soil microbial communities with increasing capacity offers the highest promise for answering many outstanding who, what, where, when, why, and with whom questions such as: Which microorganisms are linked to which soil habitats? How do microbial abundances change with changing edaphic conditions? How do microbial assemblages interact and influence one another synergistically or antagonistically? What is the full extent of soil microbial diversity, both functionally and phylogenetically? What are the dynamics of microbial communities in space and time? How sensitive are microbial communities to a changing climate? What is the role of horizontal gene transfer in the stability of microbial communities? Do highly diverse microbial communities confer resistance and resilience in soils?
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".