Relieving efforts in palm‐tree tissue sampling for population genetics analyses
Bibliographic record
Abstract
The young leaves are the main source of nucleic acids for population genetic studies in palm-trees; however, the access to this tissue may be limited by specific features of each species. Using root tissues as an alternative source of nucleic acids could facilitate the sampling in large populations.This study tests root tissue viability as an alternative nucleic acid source (root versus. leaf) and explores different protocols (tissue storage and DNA extraction methods) to obtain high-quality DNA samples.The results showed no significant differences in DNA concentration (603.7 vs. 599.1 ng/μl) and quality ratios (A260/280:2.1 vs. 1.9, and A260/230:2.1 vs. 2.0) for the comparisons of tissue source (leaf vs. root) and DNA extraction method (manual vs. kit). For tissue storage method, DNA concentration was significantly higher for root tissues stored in 70% and 90% alcohol solutions (692.8 and 822.6 ng/μl, respectively) versus those obtained from leaf tissue (603.7 ng/μl); however, for the quality parameters, no differences were found.Results showed the effective potential of using root tissue as an alternative source for nucleic acids, which could facilitate population sampling of palm-tree species for future studies, and this methodological alternative could be applied to other plant systems with similar sampling challenges. .
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".