Automated high throughput animal CO1 metabarcode classification
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
We introduce a method for assigning names to CO1 metabarcode sequences with confidence scores in a rapid, high-throughput manner. We compiled nearly 1 million CO1 barcode sequences appropriate for classifying arthropods and chordates. Compared to our previous Insecta classifier, the current classifier has more than three times the taxonomic coverage, including outgroups, and is based on almost five times as many reference sequences. Unlike other popular rDNA metabarcoding markers, we show that classification performance is similar across the length of the CO1 barcoding region. We show that the RDP classifier can make taxonomic assignments about 19 times faster than the popular top BLAST hit method and reduce the false positive rate from nearly 100% to 34%. This is especially important in large-scale biodiversity and biomonitoring studies where datasets can become very large and the taxonomic assignment problem is not trivial. We also show that reference databases are becoming more representative of current species diversity but that gaps still exist. We suggest that it would benefit the field as a whole if all investigators involved in metabarocoding studies, through collaborations with taxonomic experts, also planned to barcode representatives of their local biota as a part of their projects.
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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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