Conventional versus genomic selection for white spruce improvement: a comparison of costs and benefits of plantations on Quebec public lands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Intensive plantation forestry is a potent strategy for forest managers to increase wood production on a smaller forest land acreage, especially with the use of genetically improved reforestation stock. The main drawback with conventional conifer improvement is the several decades it takes before stock deployment, which is particularly acute in the context of climate change and evolving wood markets. Use of genomic selection allows to drastically shorten breeding cycles, resulting in more flexibility and potentially increasing benefits. This study compares the financial performance of five white spruce ( Picea glauca ) breeding and deployment scenarios, from conventional breeding to genomic selection in conjunction with top-grafting or the use of somatic embryogenesis, in the context of plantations established by the Quebec government on public lands with banned herbicide use. We estimated the land expectation value (LEV) for the five scenarios applied to eight site productivity indices, and considered costs and revenues from breeding, plantation establishment, silviculture, and harvest. LEVs at 4% discount rate were positive for all scenarios on all site indices, and varied from $197 to $2015 ha −1 assuming mechanical brushing of the plantations. The scenarios integrating genomic selection resulted in the highest LEVs, which increased with site index. We also conducted sensitivity analyses with 3% and 5% discount rates, with a range of costs and benefits, and with herbicide control of competing vegetation. These results should help orientate public investment decisions regarding the integration of genomic selection at the operational level in tree breeding and reforestation programs on public lands.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it