Exploring Angiosperms353: Developing and applying a universal toolkit for flowering plant phylogenomics
Bibliographic record
Abstract
Special Issue Introduction. \n \nTarget enrichment represents a useful, cost-effective method for researchers working on the phylogenomics of non-model organisms (e.g., Cronn et al., 2012; Hale et al., 2020). The ability to sequence a customizable predefined genomic subset for several dozens or even hundreds of taxa allows in-depth analyses and the testing of phylogenetic hypotheses in ways that were not previously possible (reviewed in McKain et al., 2018). The most popular methods for targeted sequencing of genomic loci in phylogenomics include (long-)amplicon sequencing (Rothfels et al., 2017) and hybridization capture (Mandel et al., 2014; Weitemier et al., 2014). Targeted amplicon sequencing is based on single-fragment PCR amplification or by using multiplexing methods such as a microfluidic PCR-based amplification of multiple pre-selected genomic regions (e.g., Zhang and Ozdemir, 2009; Ho et al., 2014), which can then be pooled and sequenced. Massively parallel amplicon sequencing was first used in medical diagnostics (Turner et al., 2009) and was later applied to metazoan phylogenetics (Bybee et al., 2011; O’Neill et al., 2013). Microfluidic PCR and long-amplicon sequencing were subsequently applied in plant systematics (Uribe-Convers et al., 2014, 2016; Gostel et al., 2015). Amplicon-based methods can be time consuming as they require careful optimization and validation of primers. These methods are also susceptible to many of the common problems in PCR (such as nonspecific products, inability to amplify large loci in their entirety, or simply no products). Recently, amplicon approaches have been largely supplanted by hybridization-based targeted enrichment, which allows for relatively rapid probe design with reference to a few related transcriptomes or genomes, and allows simultaneous and efficient recovery of many hundreds of genes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".