Cultivation-dependent characterization of bacterial diversity from British Columbia forest soils subjected to disturbance
Bibliographic record
Abstract
Bacteria from forest surface organic matter and mineral soil horizons were cultivated using four methods and characterized by fatty acid methyl ester (FAME) analysis. 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)) and from an unlogged reference plot (REF). Seventy-five percent of 1795 bacterial isolates were affiliated with 42 genera representing beta- and gamma-Proteobacteria, Actinobacteria, the Bacillus/Clostridium group, and the Cytophaga-Flexibacter-Bacteroides group. Approximately half of the culture collection represented genetic diversity confined to four bacterial genera: Pseudomonas, Bacillus, Paenibacillus, and Arthrobacter. A significantly higher proportion of bacterial isolates belonging to Actinobacteria, and the member genus Arthrobacter, were isolated from plot S soil samples compared with soil samples from plots N and REF. Twenty-five percent of bacterial isolates were not conclusively identified to genus with FAME analysis. Sherlock Tracker cluster analysis and partial 16S rRNA gene sequence analysis enabled classification of a subset of these isolates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".