The Trichoptera barcode initiative: a strategy for generating a species-level Tree of Life
Bibliographic record
Abstract
DNA barcoding was intended as a means to provide species-level identifications through associating DNA sequences from unknown specimens to those from curated reference specimens. Although barcodes were not designed for phylogenetics, they can be beneficial to the completion of the Tree of Life. The barcode database for Trichoptera is relatively comprehensive, with data from every family, approximately two-thirds of the genera, and one-third of the described species. Most Trichoptera, as with most of life's species, have never been subjected to any formal phylogenetic analysis. Here, we present a phylogeny with over 16 000 unique haplotypes as a working hypothesis that can be updated as our estimates improve. We suggest a strategy of implementing constrained tree searches, which allow larger datasets to dictate the backbone phylogeny, while the barcode data fill out the tips of the tree. We also discuss how this phylogeny could be used to focus taxonomic attention on ambiguous species boundaries and hidden biodiversity. We suggest that systematists continue to differentiate between 'Barcode Index Numbers' (BINs) and 'species' that have been formally described. Each has utility, but they are not synonyms. We highlight examples of integrative taxonomy, using both barcodes and morphology for species description.This article is part of the themed issue 'From DNA barcodes to biomes'.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".