Towards a DNA Barcode Reference Database for Spiders and Harvestmen of Germany
Bibliographic record
Abstract
As part of the German Barcode of Life campaign, over 3500 arachnid specimens have been collected and analyzed: ca. 3300 Araneae and 200 Opiliones, belonging to almost 600 species (median: 4 individuals/species). This covers about 60% of the spider fauna and more than 70% of the harvestmen fauna recorded for Germany. The overwhelming majority of species could be readily identified through DNA barcoding: median distances between closest species lay around 9% in spiders and 13% in harvestmen, while in 95% of the cases, intraspecific distances were below 2.5% and 8% respectively, with intraspecific medians at 0.3% and 0.2%. However, almost 20 spider species, most notably in the family Lycosidae, could not be separated through DNA barcoding (although many of them present discrete morphological differences). Conspicuously high interspecific distances were found in even more cases, hinting at cryptic species in some instances. A new program is presented: DiStats calculates the statistics needed to meet DNA barcode release criteria. Furthermore, new generic COI primers useful for a wide range of taxa (also other than arachnids) are introduced.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".