A quantitative protocol for DNA metabarcoding of springtails (Collembola)
Bibliographic record
Abstract
We developed a novel protocol with superior quantitative analysis results for DNA metabarcoding of Collembola, a major soil microarthropod order. Degenerate PCR primers were designed for conserved regions in the mitochondrial cytochrome c oxidase subunit I (mtCOI) and 16S ribosomal RNA (mt16S) genes based on published collembolan mitogenomes. The best primer pair was selected based on its ability to amplify each gene, irrespective of the species. DNA was extracted from 10 natural communities sampled in a temperate forest (with typically 25-30 collembolan species per 10 soil samples) and 10 mock communities (with seven cultured collembolan species). The two gene regions were then amplified using the selected primers, ligated with adapters for 454 technology, and sequenced. Examination of the natural community samples showed that 32 and 36 operational taxonomic units (defined at a 90% sequence similarity threshold) were recovered from the mtCOI and mt16S data, respectively, which were comparable to the results of the microscopic identification of 25 morphospecies. Further, sequence abundances for each collembolan species from the mtCOI and mt16S data of the mock communities, after normalization by using a species as the internal control, showed good correlation with the number of individuals in the samples (R = 0.91-0.99), although relative species abundances within a mock community sample estimated from sequences were skewed from community composition in terms of the number of individuals or biomass of the species. Thus, this protocol enables the comparison of collembolan communities in a quantitative manner by metabarcoding.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".