Multi‐locus species delimitation in closely related animals and fungi: one marker is not enough
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
Despite taxonomy's 250-year history, the past 20 years have borne witness to remarkable advances in technology and techniques, as well as debate. DNA barcoding has generated a substantial proportion of this debate, with its proposition that a single mitochondrial sequence will consistently identify and delimit species, replacing more evidence-rich and time-intensive methods. Although mitochondrial DNA (mtDNA) has since been the focus of voluminous discussion and case studies, little effort has been made to comprehensively evaluate its success in delimiting closely related species. We have conducted the first broadly comparative literature review addressing the efficacy of molecular markers for delimiting such species over a broad taxonomic range. By considering only closely related species, we sought to avoid confusion of success rates with those due to deeply divergent taxa. We also address whether increased population-level or geographic sampling affects delimitation success. Based on the results from 101 studies, we found that all marker groups had approximately equal success rates (~70%) in delimiting closely related species and that the use of additional loci increased average delimitation success. We also found no relationship between increased sampling of intraspecific variability and delimitation success. Ultimately, our results support a multi-locus integrative approach to species delimitation and taxonomy.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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