Temporal quantification of mating system parameters in a coastal Douglas-fir seed orchard under manipulated pollination environment
Bibliographic record
Abstract
Seed orchards main function is delivering breeding programs' gains in the form of genetically improved seedlings. They are unique experimental populations, perfectly suited for studying various pollination environments (natural or otherwise), affecting their mating system parameters. Here, under different pollination environment (natural and intrusive (pollen augmentation and/or bloom-delay)), the mating system of a second generation, wind-pollinated, coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) seed orchard was evaluated over four years. Using DNA microsatellite markers and bulk seed samples, we conducted pedigree reconstruction to assign each seed's male and female parents, followed by determining the extent of pollen contamination (external gene flow), selfing rate, and, parental gametic contribution for each year. Overall, external pollen contamination rates ranged between 10 and 28%, selfing rate varied between 12 and 17%, and 80% of the seed crops were produced by 37-64% of the orchard's parents. Pollination environment and seed crop size substantially influenced the observed results, particularly for small crops as pollen contamination was high in natural (28%) vs. intrusive pollination (10%). Generally, irrespective of the crop size, seed produced under natural pollination had higher pollen contamination, confirming the role of pollination environment manipulation in improving seed crops' genetic quality.
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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 | 0.000 | 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".