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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".