Data from: Taxonomic survey of Agaricomycetes (Fungi: Basidiomycota) in Ontario tallgrass prairies determined by fruiting body and soil rDNA sampling
Bibliographic record
Abstract
The fungal composition of North America’s grasslands is poorly known, but an important area of study due to grassland conservation concerns and their close relation to agricultural lands. This study is a survey of Agaricomcyetes from fifteen diverse tallgrass prairies across southwestern Ontario, determined through fruiting body surveys (above-ground) and next-generation sequencing of soil ribosomal DNA (below-ground), and makes comparisons between the results of these two techniques. The most species rich taxa were the Clavariaceae, Hygrophoraceae, and Entolomataceae, each detected by both techniques, with the addition of the Sebacinaceae and Polyporaceae sensu lato below-ground, and Hymenogastraceae (Hebeloma spp.) and Mycenaceae above-ground. Many of the most abundant species belonged to these species-rich taxa and were highly abundant by either technique. The above-ground surveys found at least 73 species and the below-ground technique 238 operatonal taxonomic units. Although many fine-scale taxa (species and approximate families) were unique to one technique or the other (only eight genetic species were shared between both), the below-ground technique uncovered a greater breadth of higher taxa (mostly equivalent to orders), including ones undetected by the above-ground technique. A review of grassland fungi surveys around the world shows many similarities and the potential for grassland fungal conservation in North America. Given current technological advancements and grassland conservation concerns, it is prudent to further study North America’s grassland fungi.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".