How well do ITS rDNA sequences differentiate species of true morels ( <i>Morchella</i> )?
Bibliographic record
Abstract
Arguably more mycophiles hunt true morels (Morchella) during their brief fruiting season each spring in the northern hemisphere than any other wild edible fungus. Concerns about overharvesting by individual collectors and commercial enterprises make it essential that science-based management practices and conservation policies are developed to ensure the sustainability of commercial harvests and to protect and preserve morel species diversity. Therefore, the primary objectives of the present study were to: (i) investigate the utility of the ITS rDNA locus for identifying Morchella species, using phylogenetic species previously inferred from multilocus DNA sequence data as a reference; and (ii) clarify insufficiently identified sequences and determine whether the named sequences in GenBank were identified correctly. To this end, we generated 553 Morchella ITS rDNA sequences and downloaded 312 additional ones generated by other researchers from GenBank using emerencia and analyzed them phylogenetically. Three major findings emerged: (i) ITS rDNA sequences were useful in identifying 48/62 (77.4%) of the known phylospecies; however, they failed to identify 12 of the 22 species within the species-rich Elata Subclade and two closely related species in the Esculenta Clade; (ii) at least 66% of the named Morchella sequences in GenBank are misidentified; and (iii) ITS rDNA sequences of up to six putatively novel Morchella species were represented in GenBank. Recognizing the need for a dedicated Web-accessible reference database to facilitate the rapid identification of known and novel species, we constructed Morchella MLST (http://www.cbs.knaw.nl/morchella/), which can be queried with ITS rDNA sequences and those of the four other genes used in our prior multilocus molecular systematic studies of this charismatic genus.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".