Investigating suburban micromoth diversity using DNA barcoding of malaise trap samples
Bibliographic record
Abstract
Micromoths can be challenging to identify based on morphology and are frequently omitted in assessments of moth diversity. However, their species richness and biology make them important components of terrestrial ecosystems. In this study we identified 1227 micromoths from a suburban garden at 63° north using DNA barcoding of Malaise trap samples. We recorded 78 different species with the 11 most abundant taxa accounting for 82 % of the catch. The remaining 67 species were represented by fewer than 14 specimens, but the number was often sufficient to provide a good idea of phenology. The larvae of these 78 species all feed on plants common in suburban environments. We show that when facilitated by identifications through DNA barcoding, Malaise traps provide interesting insights into the micromoth communities of suburban environments that might otherwise be overlooked. The use of Malaise traps is beneficial for investigations at high latitudes where light trapping is inefficient for sampling moths due to bright summer nights.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".