Building freshwater macroinvertebrate DNA-barcode libraries from reference collection material: formalin preservation vs specimen age
Bibliographic record
Abstract
As part of its ongoing work in biomonitoring, Environment Canada’s Canadian Aquatic Biomonitoring Network (CABIN) program has assembled an expert-verified reference collection of 3864 specimens of 604 species of Canadian freshwater macroinvertebrates. Such collections are a key resource for developing a deoxyribonucleic acid (DNA) barcode library to facilitate molecular identification of biomonitoring samples. We examined the problems encountered in using such legacy material to obtain reference barcodes. We focused on the influence of specimen age and preservation history. To supplement work on the reference collection, we determined the time-dependent effects of formalin preservation on DNA-barcode integrity in 4 common arthropod taxa by controlled exposure of fresh material obtained from laboratory cultures. Specimens in the reference collection were preserved with short-term fixation in formalin followed by prolonged preservation in 70% ethanol. Only 19 caddisfly larval specimens out of the total of 650 analyzed returned full-length sequences. In contrast, formalin preservation of freshly collected material for up to 20 d yielded good sequencing success and high-quality sequences. Freshly collected material clearly provides the best basis for the future development of DNA-barcode libraries, and formalin preservation should be avoided where possible to ensure that DNA integrity is maximized.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".