Congruence between parental reproductive investment and success determined by DNA-based pedigree reconstruction in conifer seed orchards
Bibliographic record
Abstract
Seed orchards’ parental reproductive success is often assessed through surveys of seed- and pollen-cone production (reproductive investment). Although the reliability of this approach has been questioned, its speed and ease made it the method of choice for providing approximate estimates for the genetic quality and quantity of seed orchard crops. In this study, reproductive investment data from various female and male phenotypic assessment methods from three seed orchards (lodgepole pine ( Pinus contorta Dougl. ex. Loud. var. latifolia Engelm.), Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco), and western larch ( Larix occidentalis Nutt.)) were compared with reproductive success estimates based on DNA fingerprinting and parentage analyses. The use of reproductive investment as a proxy to reproductive success on estimating seed crop’s volume genetic worth and diversity (measured by effective number of parents) was evaluated. Regression analyses indicated that the phenotypic assessment methods reflect the true parental reproductive success and that estimates of seed- and pollen-cone volume production appear to be the most appropriate indicators of female and male reproductive success, respectively. Although the best combination of female–male survey methods produced inflated effective number of parents estimates, the genetic worth estimates were similar to those based on DNA, highlighting their robustness to fertility variation assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".