Bacterial diversity associated with subalpine fir (<i>Abies lasiocarpa</i>) ectomycorrhizae following wildfire and salvage-logging in central British Columbia
Bibliographic record
Abstract
To assess the effect of fire and salvage logging on the diversity of mycorrhizal-bacterial communities, bacteria associated with Cenococcum, Thelephora, Tomentella, Russulaceae, and E-strain ectomycorrhizae (ECM) of Abies lasiocarpa seedlings were characterized using two approaches. First, bacteria were isolated and characterized by Biolog, gas chromatography fatty acid methyl ester (GC-FAME), and amplified 16S rDNA restriction analysis (ARDRA). The bacterial communities retrieved from ECM from both sites were dominated by Proteobacteria (groups gamma and beta). Pseudomonas was the most common genus isolated, followed by Variovorax, Burkholderia, and Xanthomonas. Gram-positive isolates (mostly high-G+C Gram-positive bacteria) were more frequently retrieved on the burned-salvaged site, many commonly associated with the two ascomycete ECM, Cenococcum and E-strain. Pseudomonas species were retrieved more frequently from Thelephora. Although actinomycetes were isolated from all sites, almost no actinomycetes or other Gram-positive bacteria were isolated from either Thelephora or Tomentella. Second, amplified 16S rRNA gene sequences were amplified directly from root tips and then cloned into the plasmid vector pAMP1, followed by restriction analysis. This technique distinguished more genotypes than isolates retrieved by culturing methods, but generally, results were similar in that the largest proportion of the bacteria were putatively Gram-negative; putative Gram-positive bacteria were fewer and most were from the burned-salvaged site. Direct cloning resulted in many patterns that did not match any identified isolates, suggesting that a large proportion of clones were unique or not culturable by the methods used. Analysis for both protocols showed no significant difference in bacterial diversity between the burned-salvaged and unburned sites.
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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 | 0.001 |
| 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) | 0.001 | 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".