Improved AFLP analysis of tree species
Bibliographic record
Abstract
Amplified fragment length polymorphism (AFLP) is a high-throughput, molecular-marker technique that is used increasingly in a variety of genetic analyses. Here, the conditions for carrying out AFLP analysis have been established for different tree species, including both angiosperm and gymnosperm trees, with genome sizes ranging from 0.54 to 38 pg DNA/2C. Specific parameters have been determined to provide informative and reproducible AFLP fingerprints of peach (Prunus persica L.), eucalypt, oak, poplar, and loblolly pine (Pinus taeda L.). Typically, 80-130 amplified DNA fragments (i.e., loci analyzed per primer combination) were obtained. Subsequently, these AFLP conditions were evaluated for intra- and inter-specific genetic variability studies as well as for genome mapping purposes of woody species. This work demonstrates that AFLP is a powerful tool in forest tree genetics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".