DNA barcodes for soil animal taxonomy
Bibliographic record
Abstract
The biodiversity of soil communities remains very poorly known and understood. Soil biological sciences are strongly affected by the taxonomic crisis, and most groups of animals in that biota suffer from a strong taxonomic impediment. The objective of this work was to investigate how DNA barcoding - a novel method using a microgenomic tag for species identification and discrimination - permits better evaluation of the taxonomy of soil biota. A total of 1,152 barcode sequences were analyzed for two major groups of animals, collembolans and earthworms, which presented broad taxonomic and geographic sampling. Besides strongly reflecting the taxonomic impediment for both groups, with a large number of species-level divergent lineages remaining unnamed so far, the results also highlight a high level (15%) of cryptic diversity within known species of both earthworms and collembolans. These results are supportive of recent local studies using a similar approach. Within an impeded taxonomic system for soil animals, DNA-assisted identification tools can facilitate and improve biodiversity exploration and description. DNA-barcoding campaigns are rapidly developing in soil animals and the community of soil biologists is urged to embrace these methods.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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".