Arbuscular mycorrhizal fungi assemblages in Chernozem great groups revealed by massively parallel pyrosequencing
Bibliographic record
Abstract
The arbuscular mycorrhizal (AM) fungal resources present in wheat fields of the Canadian Prairie were explored using 454 pyrosequencing. Of the 33 dominant AM fungal operational taxonomic units (OTUs) found in the 76 wheat fields surveyed at anthesis in 2009, 14 clustered as Funneliformis - Rhizophagus, 16 as Claroideoglomus, and 3 as Diversisporales. An OTU of Funneliformis mosseae and one OTU of Diversisporales each accounted for approximately 16% of all AM fungal OTUs. The former was ubiquitous, and the latter was mainly restricted to the Black and Dark Brown Chernozems. AM fungal OTU community composition was better explained by the Chernozem great groups (P = 0.044) than by measured soil properties. Fifty-two percent of the AM fungal OTUs were unrelated to measured soil properties. Black Chernozems hosted the largest AM fungal OTU diversity and almost twice the number of AM fungal sequences seen in Dark Brown Chernozems, the great group ranking second for AM fungal sequence abundance. Brown Chernozems hosted the lowest AM fungal abundance and an AM fungal diversity as low as that seen in Gray soils. We concluded that Black Chernozems are most conducive to AM fungal proliferation. AM fungi are generally distributed according to Chernozem great groups in the Canadian Prairie, although some taxa are evenly distributed in all soil groups.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".