DNA-based detection and identification of Glomeromycota: the virtual taxonomy of environmental sequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it