Molecular characterization of bacterial diversity from British Columbia forest soils subjected to disturbance
Bibliographic record
Abstract
Bacteria from forest soils were characterized by DNA sequence analysis of cloned 16S rRNA gene fragments (16S clones). Surface organic matter and mineral soil samples from a British Columbia Ministry of Forests Long-Term Soil Productivity (LTSP) installation were collected during winter and summer from two disturbance treatments: whole-tree harvesting with no soil compaction (plot N) and whole-tree harvesting plus complete surface organic matter removal with heavy soil compaction (plot S). Phylogenetic analyses revealed that 87% of 580 16S clones were classified as Proteobacteria, Actinobacteria, Acidobacterium, Verrucomicrobia, Bacillus/Clostridium group, Cytophaga-Flexibacter-Bacteroides group, green nonsulfur bacteria, Planctomyces, and candidate divisions TM6 and OP10. Seventy-five 16S clones could not be classified into known bacterial divisions, and five 16S clones were related to chloroplast DNA. Members of Proteobacteria represented 46% of the clone library. A higher proportion of 16S clones affiliated with y-Proteobacteria were from plot N compared with plot S. 16S rRNA gene fragments amplified with Pseudomonas-specific primers and cloned (Ps clones) were examined from mineral-soil samples from plots N and S from three LTSP installations. A significantly greater proportion of sequenced Ps clones from plot N contained Pseudomonas 16S rRNA gene fragments compared with Ps clones from plot S.
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".