A comparative evaluation of the application of somatic embryogenesis, rooting of cuttings, and organogenesis of conifers
Bibliographic record
Abstract
Vegetative propagation of conifers has found large-scale industrial application via somatic embryogenesis (SE), rooting of cuttings, and organogenesis. Genetic gain is achieved with all of these methods but is the highest with SE, primarily because SE cultures can be cryopreserved. This allows for plants derived from part of each cell line to be field tested over a long period while the rest of each cell line is kept in a juvenile state by cryopreservation for later use. This makes it possible to select the best performers within the best families. For rooting of cuttings and organogenesis, genetic gain is generally based on family average, which is less powerful. However, SE has its limitations, primarily because its initiation, maturation, or germination rates are too low to be effective for many species. Consequently, for many species, the preferred clonal propagation option is still rooting of cuttings or organogenesis. If better methods can be developed to keep ortets used for rooting of cuttings and organogenesis in a prolonged juvenile state, or if future developments in marker technology reach a point where within-family selection becomes possible without the aid of cryopreservation, rooting of cuttings and organogenesis will achieve the same genetic gain as SE.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".