Pollen contamination and mating patterns in a Douglas-fir seed orchard as measured by simple sequence repeat markers
Bibliographic record
Abstract
Pollen contamination is detrimental to the genetic quality of seed orchard crops. Highly variable simple sequence repeat (SSR) markers make it possible to accurately measure pollen contamination and characterize patterns of within-orchard mating by directly identifying the male and female parent of each seed produced in the orchard. We used nine SSR markers to measure pollen contamination and characterize mating patterns based on seed samples collected in 3 years (1999, 2000, and 2003) from one block of a nonisolated, open-pollinated, clonal seed orchard of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in western Oregon. Pollen contamination was consistently high across the 3 years (mean = 35.3%, range = 31.0%-41.3%) and appeared to result primarily from cross-pollination among the orchard blocks. Levels of pollen contamination varied substantially among clones and were higher in clones with early female receptivity (mean = 55.5%) than in those with either mid (mean = 36.4%) or late (mean = 28.3%) female receptivity. We detected low rates of self-pollination (mean = 1.8% per clone) and over 10-fold differences in the relative paternal contributions of the clones. There was a clear pattern of positive assortative mating with respect to floral phenology. This study illustrates that SSR markers are powerful tools for characterizing seed lots and improving the design and management of Douglas-fir seed orchards.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".