Validation of an Effective Protocol for Culicoides Latreille (Diptera: Ceratopogonidae) Detection Using eDNA Metabarcoding
Bibliographic record
Abstract
eDNA metabarcoding is an effective molecular-based identification method for the biosurveillance of flighted insects. An eDNA surveillance approach maintains specimens for secondary morphological identification useful for regulatory applications. This study identified Culicoides species using eDNA metabarcoding and compared these results to morphological identifications of trapped specimens. Insects were collected using ultraviolet (UV) lighted fan traps containing a saturated salt (NaCl) solution from two locations in Guelph, Ontario, Canada. There were forty-two Culicoides specimens collected in total. Molecular identification detected four species, C. biguttatus, C. stellifer, C. obsoletus, and C. mulrennani. Using morphological identification, two out of these four taxonomic ranks were confirmed at the species level (C. biguttatus and C. stellifer) and one was confirmed at the subgenus level (Avaritia [C. obsoletus]). No molecular detection of Culicoides species occurred in traps with an abundance of less than three individuals per taxon. The inconsistency in identifying Culicoides specimens to the species level punctuates the need for curated DNA reference libraries for Culicoides. In conclusion, the saturated salt (NaCl) solution preserved the Culicoides’ morphological characteristics and the eDNA.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".