Distantly sampled soils carry few species in common
Bibliographic record
Abstract
The bacterial phylogenetic structure of soils from four distinctly different sites in South and North America was analyzed. One hundred and thirty-nine thousand sequences of the V9 region of the small subunit of the bacterial ribosomal RNA gene generated for a previous study were used for this work. Whereas the previous work estimated levels of species richness, this study details the degree of bacterial community overlap between the four soils. Sequences from the four soils were classified and grouped into different phyla and then assigned to operational taxonomic units (OTUs) as defined by 97 or 100% sequence similarity. Pairwise Jaccard and theta similarity indices averaged over all phyla equalled 6 and 12% respectively at the 97% similarity level, and 15% for both at the 100% similarity level. At 100 and 97% sequence similarity, 1.5 and 4.1% of OTUs were found in all four soils respectively, and 87.9 and 74.4%, respectively were a unique particular soil. These analyses, based on the largest soil bacterial sequence retrieval to date, establish the high degree of community structure difference for randomly sampled dissimilar soils and support the idea that wide sampling is important for bioprospecting. The 10 most abundant cultured genera were determined in each soil. These 10 genera comprised a significant proportion of the reads obtained from each soil (31.3-37.4%). Chitinophaga was the most abundant or the second most abundant genus in all four soils with 7.5-13.8% of the total bacterial sequences in these soils. The striking result is that several culturable genera, whose roles in soil are virtually unknown, were found among these dominant sequences.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".