Breeding Values of Parental Trees, Genetic Worth of Seed Orchard Seedlots, and Yields of Improved Stocks in British Columbia
Bibliographic record
Abstract
Abstract This report describes the procedures that are currently used in British Columbia for predicting the breeding values of parents, estimating the genetic worth of orchard seedlots, and projecting the yields of genetically improved stocks. Breeding value is a measure of the genetic quality of an individual as a parent. There are several procedures available for its estimation/prediction. Among those, the best linear prediction (BLP) relaxes most of the assumptions required by the others and minimizes the error variance of prediction. In most situations in British Columbia, it should provide predictions with satisfactory accuracy and precision with greatly reduced computational complexity. In this province, breeding value for growth potential is expressed as percent gain of stem volume over the unimproved population at a designated rotation age.Genetic worth is an important attribute of the genetic quality of a seedlot. It represents the average level of genetic gain expected for the trait of concern at a designated rotation age when a seedlot is used for reforestation. Currently, the genetic worth of a seedlot is estimated by the mean breeding value of all the parents, including those that contribute to pollen contamination and supplemental mass pollination, weighted by their proportional gamete contributionsThe yield of a genetically improved plantation is projected by incorporating the genetic worth of the seedlot into the existing growth model developed based on extensive data from managed unimproved stands. The current approach not only takes account of the stand dynamics determined by site conditions and silvicultural regimes but also the declining nature of expected gain over time because of imperfect age-age genetic correlation.Because of errors from genetic and environmental sampling, measurement, and analysis, as well as possible violation of model assumptions, estimates/predictions may still be subject to errors and/or biases. Various conservative measures have been taken to minimize any possible upwards biases. As more matured data and advanced analytical technologies become available, both the accuracy and precision will be improved. The advancement made in the procedures described in this document should contribute to superior decisions in many aspects of forest management. West. J. Appl. For. 18(2):88–100.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".