Development of a core SNP panel for cacao (<i>Theobroma cacao</i> L.) identity analysis
Bibliographic record
Abstract
Single nucleotide polymorphisms (SNPs) are preferred markers for DNA fingerprinting and diversity studies in cacao (Theobroma cacao L.). Yet, a consensus SNP panel with a minimum number of SNPs for optimal identity analysis is unavailable for cacao. An initial set of 146 SNP panels of varying sizes were assembled based on heterozygosity, linkage disequilibrium (LD), linkage group (LG) distribution, major allele frequency, minor allele frequency (MiAF), polymorphism information content (PIC), and random distribution. These panels were assessed to determine their ability to distinguish among a training set of 155 accessions. The panels with the best separation ability were supplemented with additional SNPs to create 16 designer panels, which separated all 155 accessions. The 16 designer SNP panels were then assessed on a dataset of 1220 accessions coming from 10 ancestral groups. Increasing the number of SNPs generally yielded improved resolution of genetic identities with concomitant reduction of synonymous groups. The number and choice of SNPs were critical factors with LD, MiAF, and PIC being important selection attributes but an even LG distribution was unnecessary. A robust set of 96 SNPs is recommended as a minimal core SNP panel for cacao DNA fingerprinting to the international cacao community.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".