Megabarcoding dark taxa – Assessing the utility of mass DNA barcoding for phorid fly species discovery
Bibliographic record
Abstract
Many hyperdiverse, small-bodied insect families contain numerous undescribed species, generally termed "dark taxa". Scuttle flies (Diptera: Phoridae), being among the most diverse insect groups globally, are a prime example. DNA barcoding can help delineate dark taxa, particularly when integrated with morphology, and/or additional molecular evidence. We sequenced COI-barcodes from 9,120 Finnish phorid specimens and initially identified them using the BOLD database. Furthermore, species identifications of all 843 specimens, belonging to other genera than Megaselia, were confirmed morphologically. Initially, the BOLD-based identifications matched the morphological identifications only in 68% of the cases, primarily due to numerous misidentifications in BOLD. After adjusting the BOLD reference identifications based on morphological analyses of male features, we established a reliable framework for female identification. This is advantageous for future identification of females, as they are often excluded from traditional identification keys. Only two species were discovered as new to Finland, demonstrating that Finnish fauna is well-known, except Megaselia. Although DNA barcodes show great promise for identifying phorids, incorrectly identified reference sequences remain challenging, not the functionality of barcode gene Cytochrome c oxidase subunit I (COI) itself. The number of Megaselia BINs greatly exceeded the known Finnish species count, with many sequences lacking matches in BOLD. This further highlights Megaselia as a particularly dark group, for which genetic tools are essential for uncovering species identities and assessing diversity.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".