Use of different seed tissues for separate biparentage identification of dispersed seeds in conifers: confirmations and practices for gene flow in <i>Pinus densiflora</i>
Bibliographic record
Abstract
To investigate how accurately biparentage assignments for coniferous seeds can be improved by using different kinds of seed tissues, we assigned biparentage to dispersed seeds in a natural stand of Pinus densiflora Siebold & Zucc. (288 mature trees) using two procedures: with or without megagametophyte haplotype data (“MH” and “ordinary” procedures, respectively). Using archived clones, we confirmed the conifer-specific modes of inheritance of three kinds of seed tissues from certain maternal trees using six microsatellite markers. In the natural stand, under the MH procedure, 39.2% of male parents and 77.0% of female parents for a total of 204 seeds analyzed were assigned to at least one mature tree within the study area. At that time, the proportion of exactly matching seeds out of seeds with one matching parent under the MH procedure was significantly larger than that of the seeds under the ordinary procedure. The biparentage assignments under the ordinary procedure corresponded to only 53.7% of the accurate separate assignments under the MH procedure. It is suggested that analyzing different seed tissues is effective for exact and accurate biparentage assignments in investigations of biparentally mediated gene flow in coniferous populations, particularly at the seed-dispersal stage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".