Biogeography and genotypic diversity of <i>Metarhizium brunneum</i> and <i>Metarhizium robertsii</i> in northwestern North American soils
Bibliographic record
Abstract
The biogeography and genotype diversity of Metarhizium species in northwestern North American soils was examined; 20 ecoregions were sampled, including 58 agricultural and 80 natural habitat subsites, and areas that were glaciated during the Pleistocene epoch. One hundred and twenty-nine isolates of M. brunneum, 26 isolates of M. robertsii, four isolates of M. guizhouense, one isolate of M. flavoviride, and 55 isolates of Beauveria were recovered. Metarhizium and Beauveria species were isolated in diverse ecoregions within the study area, but a trend for increased isolation of Metarhizium species in western regions of the study area was observed. Consistent with this observation, the prevalence of M. brunneum and M. robertsii decreased at higher elevations, and the opposite was true for Beauveria. Both M. brunneum and M. robertsii were more commonly isolated from agricultural and natural habitat subsites, and considerable genotypic diversity was observed in both habitats and within the same subsite. Metarhizium robertsii, but not M. brunneum, was more commonly isolated from nonglaciated locations; however, less diversity and richness was observed for M. brunneum recovered from glaciated versus nonglaciated locations consistent with insular biogeography. The study has implications for microbial control strategies in the region.
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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 | 0.000 | 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) | 0.000 | 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".