<scp>DNA</scp> barcode‐based delineation of putative species: efficient start for taxonomic workflows
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
The analysis of DNA barcode sequences with varying techniques for cluster recognition provides an efficient approach for recognizing putative species (operational taxonomic units, OTUs). This approach accelerates and improves taxonomic workflows by exposing cryptic species and decreasing the risk of synonymy. This study tested the congruence of OTUs resulting from the application of three analytical methods (ABGD, BIN, GMYC) to sequence data for Australian hypertrophine moths. OTUs supported by all three approaches were viewed as robust, but 20% of the OTUs were only recognized by one or two of the methods. These OTUs were examined for three criteria to clarify their status. Monophyly and diagnostic nucleotides were both uninformative, but information on ranges was useful as sympatric sister OTUs were viewed as distinct, while allopatric OTUs were merged. This approach revealed 124 OTUs of Hypertrophinae, a more than twofold increase from the currently recognized 51 species. Because this analytical protocol is both fast and repeatable, it provides a valuable tool for establishing a basic understanding of species boundaries that can be validated with subsequent studies.
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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 | 0.000 | 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) | 0.002 | 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