An updated and extended version of the Melastomataceae probe set for target capture
Bibliographic record
Abstract
Premise: A probe set was previously designed to target 384 nuclear loci in the Melastomataceae family; however, when trying to use it, we encountered several practical and conceptual problems, such as the presence of sequences in reverse complement, intronic regions with stop codons, and other issues. This raised concerns regarding the use of this probe set for sequence recovery in Melastomataceae. Methods: In order to correct these issues, we cleaned the Melastomataceae probe set, extended it with additional sequences, and compared its performance with the original version. Results: The final probe set targets 396 putative nuclear loci represented by 6009 template sequences. The probe set has been made available, along with details on the cleaning process, for reproducibility. We show that the new probe set performs better than the original version in terms of sequence recovery. Discussion: This updated, extended, and cleaned probe set will improve the availability of phylogenomic resources across the Melastomataceae family. It is fully compatible with sequence recovery and extraction pipelines. The cleaning process can also be applied to any plant-targeting probe set that would need to be cleaned or updated if new genomic resources for the targeted taxa become available.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 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".