DNA-based detection and identification of Glomeromycota: the virtual taxonomy of environmental sequences
Bibliographic record
Abstract
An increasing number of case studies are reporting Glomeromycota molecular diversity from ecosystems worldwide. Typically, phylogroups that can be related to morphospecies and those that remain unidentified (“environmental”) are recorded. To compare such data and generalise observed patterns, the principles underlying sequence identification should be unified. Data from case studies are collected and systematized in a public database MaarjAM ( http://www.maarjam.botany.ut.ee ), which applies a unique molecular operational taxonomic unit (MOTU) nomenclature: virtual taxa (VT) are phylogenetically defined sequence groups roughly corresponding to species-level taxa. VT are based on type sequences, making them consistent in time, but they also evolve: they can be split or merged, when necessary. This system allows standardisation of original MOTU designations and, much like binomial taxonomic nomenclature, comparison and consistency between studies. Refinement of VT delimitation principles and comparability with traditional Glomeromycota taxonomy will benefit from more information about intra- vs. inter-specific nucleotide variation in Glomeromycota, sequencing of morphospecies, and resolution of issues in Glomeromycota taxonomy. As the recorded number of VT already exceeds the number of Glomeromycota morphospecies, designation of species based on DNA alone appears a necessity in the near future. Application of VT is becoming widespread, and MaarjAM database is increasingly used as a reference for environmental sequence identification. The current status and future prospects of arbuscular mycorrhizal fungi (AMF) DNA-based identification and community description are presented.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".