Development and characterization of microsatellite markers for analysis of population differentiation in the tree legume<i>Acacia koa</i>(Fabaceae: Mimosoideae) in the Hawaiian Islands
Bibliographic record
Abstract
The aim of this research was to develop and use microsatellite markers to characterize the high-value timber tree Acacia koa (koa), which is endemic to the Hawaiian Islands. Genomic DNA fragments of 300-1000 bp were cloned and sequenced following enrichment for microsatellite motifs by PCR using 7 oligonucleotide repeat primers in separate reactions. Among 96 sequences analyzed, 63 contained unique microsatellite motifs flanked by variable sequences. A dual PCR method involving a primer walking step was used to develop 15 primer pairs. Another 16 primer pairs were developed directly from the variable sequences on both sides of the microsatellite motifs. These 31 primer pairs were tested on 172 koa plants representing 11 populations collected from 4 of the major Hawaiian Islands. Nine of the primers that identified polymorphic microsatellite loci and 3 that detected unique alleles exclusively in some populations were used for genetic diversity studies of koa. Cluster analysis and multidimensional scaling of the allelic phenotype data revealed that koa from Kauai formed a distinct group separate from koa of the neighboring islands of Oahu, Maui, and Hawaii. The oldest of the four islands, Kauai, also had the most diverse populations of koa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".