Benefitcost analysis of DNA marker-based selection in progenies of<i>Pinus radiata</i>seed orchard parents
Bibliographic record
Abstract
The financial viability of DNA marker-based within-family selection (MBS) compared with full-sib family forestry was evaluated for Pinus radiata Donn. ex D. Don. Two traits were investigated: wood density (WD) and diameter at breast height (DBH, 1.4 m). Assuming 20 biallelic loci of equal additive effect controlling trait variation in 15 unrelated top full-sib families of P. radiata, marginal costs of quantitative trait loci (QTL) detection and selection were estimated based on an average of slightly less than five loci per family. We assumed a program where 10 genotypes per family per year were deployed over a 5-year period, and each replicated 100 000 times via fascicle cuttings methods. Estimated marginal costs were NZ$32 and NZ$72 per 1000 plants for WD and DBH, respectively. Genotyping costs were the single largest component for both traits. Genetic gains were estimated by modifying predicted log volumes (DBH) or proportion of structural-grade timber (WD) with and without pruning. Estimated genetic gains ranged from 3.2 to 3.4%. Net present values (assuming a 9.5% discount rate) ranged from an average of NZ$51 to NZ$621/ha. Results showed that MBS for DBH was more profitable than for WD, despite markedly higher costs of QTL detection. All trait-silviculture combinations showed financial gains with internal rates of return of 9% or greater, even when estimated revenues were decreased 70% from forecast revenues. While this analysis is based on a large number of assumptions, it is robust and the results show that significant financial gains from MBS are possible even when selection is based upon DNA markers linked to a few loci each of relatively small effect.
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 | 0.001 |
| 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) | 0.001 | 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".