Unexpectedly High Bacterial Diversity in Arctic Tundra Relative to Boreal Forest Soils, Revealed by Serial Analysis of Ribosomal Sequence Tags
Bibliographic record
Abstract
Arctic tundra and boreal forest soils have globally relevant functions that affect atmospheric chemistry and climate, yet the bacterial composition and diversity of these soils have received little study. Serial analysis of ribosomal sequence tags (SARST) and denaturing gradient gel electrophoresis (DGGE) were used to compare composite soil samples taken from boreal and arctic biomes. This study comprises an extensive comparison of geographically distant soil bacterial communities, involving the analysis of 12,850 ribosomal sequence tags from six composite soil samples. Bacterial diversity estimates were greater for undisturbed arctic tundra soil samples than for boreal forest soil samples, with the highest diversity associated with a sample from an extreme northern location (82(o)N). The lowest diversity estimate was obtained from an arctic soil sample that was disturbed by compaction and sampled from a greater depth. Since samples from the two biomes did not form distinct clusters on the basis of SARST data and DGGE fingerprints, factors other than latitude likely influenced the phylogenetic compositions of these communities. The high number of ribosomal sequences analyzed enabled the identification of possible cosmopolitan and endemic bacterial distributions in particular soils.
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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) | 0.002 | 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".