Spruce
Bibliographic record
Abstract
Abstract Over the last decade, the production of transgenic conifers, including spruces, has been greatly facilitated by the advancement in somatic embryogenesis whereby embryogenic tissue is transformed through co‐cultivation with Agrobacterium tumefaciens or DNA‐coated microprojectiles. This has facilitated mass propagation of transgenic plants and the development of robust protocols for the genetic transformation of spruce providing, for the first time, the magnitude‐of‐scale required for implementing functional genomics studies in conifers. Presently, forest genomics research focuses on the identification and characterization of gene function that control major traits and attributes in forest trees. Functional genomic research has provided a greater understanding of the roles of specific genes in trees that will lead to the development of selection tools to accurately identify elite trees, which is particularly important in forestry where tree breeding cycles are very long. In this chapter, we first briefly review the biology of the genus Picea and subsequently expose practical aspects of conventional breeding. We then examine technical issues related to genetic engineering. Finally, we discuss the biosafety issues and considerations related to the deployment of transgenic trees.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.030 |
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".