Propylene glycol-based antifreeze is an effective preservative for DNA metabarcoding of benthic arthropods
Bibliographic record
Abstract
Preservation of DNA in bulk environmental samples is conventionally achieved using ethanol; however, transportation restrictions on ethanol, particularly from remote locations, are problematic, and ethanol requires a lengthy evaporation period to avoid polymerase chain reaction inhibition. We examined the efficacy of an easily accessible, non-toxic, propylene glycol-based antifreeze as an alternative to molecular-grade ethanol for preserving macroinvertebrate DNA from bulk-benthos DNA samples. We used 2 processing methods (no evaporation of preservative vs full evaporation) to test the differences in both cytochrome oxidase I (COI) exact sequence variants (ESVs) and COI taxonomic orders detected in both ethanol- and antifreeze-preserved samples. Our results suggest that antifreeze is a suitable alternative to ethanol for preservation of DNA in freshly collected samples (e.g., up to 3 d) because of the comparable ESV richness detected in antifreeze-preserved samples. We have demonstrated that by using antifreeze, it is possible to achieve sufficient taxonomic coverage and assess macroinvertebrate assemblages within bulk-benthos DNA samples. The application of this non-regulated preservative is particularly important for remote sampling (i.e., only air accessible) and sampling for community-based biomonitoring projects within Indigenous territories where alcohol is prohibited or not available.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".