Decoding ice plants: challenges associated with barcoding and phylogenetics in the diverse succulent family Aizoaceae
Bibliographic record
Abstract
Aizoaceae is the largest succulent plant family in the world, including in excess of 1800 species. Despite its richness, a large proportion of its taxa are listed as data deficient and as such, has been identified as the top priority for taxonomic research in South Africa. Limitations to accurate taxonomic identification of taxa in the family may be partly attributed to the degree of technical knowledge required to identify taxa in the Aizoaceae. DNA barcoding may provide an alternative method of identification; however, the suitability of commonly used gene regions has not been tested in the family. Here, we analyse variable and parsimony informative characters (PIC), as well as the barcoding gap, in commonly used plastid regions (atpB-rbcL, matK, psbA-trnH, psbJ-petA, rpl16, rps16, trnD-trnT, trnL-trnF, trnQ-rps16, and trnS-trnG) and the nuclear region ITS (for Aizooideae only) across two subfamilies and two expanded clades within the Aizoaceae. The relative percentage of PIC was much greater in subfamilies Aizooideae and Mesembryanthemoideae than in Ruschioideae. Although nrITS had the highest percentage of PIC, barcoding gap analyses identified neither ITS nor any chloroplast region as suitable for barcoding of the family. From the results, it is evident that novel barcoding regions need to be explored within the Aizoaceae.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".