Data from: The importance of taxonomic resolution for additive beta diversity as revealed through DNA barcoding
Bibliographic record
Abstract
Additive diversity partitioning (α, β, and γ) is commonly used to study the distribution of species-level diversity across spatial scales. Here, we first investigate whether published studies of additive diversity partitioning show signs of difficulty attaining species-level resolution due to inherent limitations with morphological identifications. Second, we present a DNA barcoding approach in order to delineate specimens of stream caddisfly larvae (order Trichoptera) and considered the importance of taxonomic resolution on classical (additive) measures of beta (β) diversity. Caddisfly larvae were sampled using a hierarchical spatial design in two regions (sub-Arctic Churchill, Manitoba, Canada; temperate Pennsylvania, USA) and then additively partitioned according to Barcode Index Numbers (molecular clusters that serve as a proxy for species), genus, and family levels; diversity components were expressed as proportional species turnover. We screened 114 articles of additive diversity partitioning and found that a third reported difficulties with achieving species-level identifications, with a clear taxonomic tendency towards challenges identifying invertebrate taxa. Regarding our own study, caddisfly BINs appeared to show greater sub-regional turnover (e.g. proportional additive β) compared to genus or family levels. Diversity component studies failing to achieve species resolution due to morphological identifications may therefore be underestimating diversity turnover at larger spatial scales.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".