OTU Delimitation with Earthworm DNA Barcodes: A Comparison of Methods
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
Although DNA barcodes-based operational taxonomic units (OTUs) are increasingly used in earthworm research, the relative efficiency of the different methods available to delimit them has not yet been tested on a comprehensive dataset. For this study, we used three datasets containing 651, 2304 and 4773 COI barcodes of earthworms from French Guiana, respectively, to compare five of these methods: two phylogenetic methods—namely Poisson Tree Processes (PTP) and General Mixed Yule Coalescence (GMYC)—and three distance matrix methods—namely Refined Single Linkage (RESL, used for assigning Barcode Index Numbers in the Barcode of Life Data systems), Automatic Barcode Gap Discovery (ABGD), and Assemble Species by Automatic Partitioning (ASAP). We found that phylogenetic approaches are less suitable for delineating OTUs from DNA barcodes in earthworms, especially for large sets of sequences. The computation times are unreasonable, they often fail to converge, and they also show a strong tendency to oversplit species. Among distance-based methods, RESL also has a clear tendency to oversplitting, while ABGD and ASAP are less prone to mismatches and have short computation times. ASAP requires less a priori knowledge for model parameterisation than AGBD, provides efficient graphical outputs, and has a much lower tendency to generate mismatches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it