Reduced diversity of AM fungi in annually cropped fields of the Canadian prairie
Bibliographic record
Abstract
The arbuscular mycorrhizal (AM) fungi are important service providers to agriculture that are impacted by certain cultural practices. We assessed the status of AM fungal resources in the Prairie, from the Grey to the Brown soil zone, through sampling 176 wheat fields, 117 adjacent roadsides and 24 natural areas, at heading. The 18S rRNA gene sequences of AM fungi in soil metagenomic DNA accounted for 15% of all fungal target sequences recorded, and clustered in 122 operational taxonomic units (OTUs), a proxy for species. A few OTUs dominated under each land use type, and these OTUs often dominated under all three types of land use. The AM fungal community was influenced by land use and soil type. The size of the family Claroideoglomeraceae decreased from Black to Brown soils, and from roadside to field soil. On average, about eight OTUs were found in a field or a natural soil as compared to 14 in roadsides, and diversity (Shannon index) was lowest in cropland where the community was very uneven. The diversity and abundance of AM fungi was low in Brown soils. Lower AM fungal abundance and diversity in 2009, the dry year, than in 2010, the wet year, suggests that the proliferation of AM fungi in Brown soils may be limited by water availability. A long history of frequent fallow and wheat monoculture in the Brown soil zone may also explain reduced AM fungal diversity in these dry soils.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".