New technological developments in cutting propagation to increase forest productivity in Quebec
Bibliographic record
Abstract
Mass cutting propagation is largely used worldwide to reproduce elite material obtained from the best controlled crosses, as a means to increase forest productivity. In Quebec, the production of conifer cuttings has steadily increased since the opening of the Cutting propagation center of the Pépinière forestière de Saint-Modeste (Saint-Modeste Forest Nursery), in 1989. Several species (white spruce, black spruce, Norway spruce and hybrid larch) are now propagated using two unique and complementary systems (the “Bouturathèques ” and double-walled enclosures) developed by the ministère des Ressources naturelles et de la Faune du Québec. Close collaboration between researchers of the Direction de la recherche forestière (Forest Research Directorate) and practicans of the Direction générale des pépinières et des stations piscicoles (Nurseries and Fish farms Directorate) has led to adapted culture scenarios for each species (stock plant culture, rooting conditions, and culture regimes for the production of large-size plants). This collaboration also facilitates the take-on of new challenges, such as the integration of somatic embryogenesis, the development of alternative culture scenarios and the characterization of controlled crosses. The double-walled enclosure system has now been implemented in two other public nurseries (Berthier and Grandes-Piles), for the propagation of white spruce cuttings, in a first step. In 2007, 5.15
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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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".