Pollen pool heterogeneity in jack pine (Pinus banksiana Lamb.) : a problem for estimating population outcrossing rate? / Yong-Bi Fu
Bibliographic record
Abstract
Pollen pool heterogeneity, which violates an assumption \nof the mixed-mating model, is one of the major problems \nfacing population geneticists concerned with measuring \nplant mating systems. In the present study, isozyme markers \nwere used to examine pollen pool heterogeneity in two \nnatural populations of jack pine, Pinus banksiana Lamb.,in \nnorthwestern Ontario, Canada. Population multilocus \nestimates of outcrossing rate ranged from 0.829 to 0.952 \nand differed significantly between populations. Singletree \noutcrossing rates were found to be homogeneous among \ntrees in both populations. Computer simulation studies \nshoved that the consanguineous mating pollen pool was a \npotentially important component of the pollen pool, capable \nof biasing population outcrossing estimates downward. In \ncontrast, random heterogeneity of the pollen pool was found \nto have no effect on population estimates of outcrossing \nrates. Pollen pool heterogeneity existed in these two \nnatural populations. However, it appeared to be random in \nnature and therefore did not affect the population \noutcrossing estimates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".