Effect of drying treatments on warping of 36-year-old white spruce seed sourcestested in a provenance trial
Bibliographic record
Abstract
Wood from plantations will increasingly become a major source of supply for the lumber industry and this raw material is likely to have characteristics much different from those of the wood harvested in natural forests.This could require costly adjustments to manufacturing processes to maintain the quality of the end-use products.In Canada, white spruce (Picea glauca [Moench] Voss) is one of the main reforestation species and one of the most extensively used for lumber.In this study we investigated the genetic variation in warping in kiln drying of 25 white spruce provenances grown in a plantation and one from a second-growth forest stand.All of them were from the Great Lakes -St.Lawrence region.Two drying treatments were applied, i.e. conventional and high-temperature drying.For bow, crook and twist warp defects after drying, significant differences were not found among the provenances tested, nor between the drying treatments.However, significant differences were revealed between the mean of all provenances (plantation-grown) and wood from second-growth forests for crook and twist defects.The high proportion of tree to tree variation provides grounds for hope of rapid gains through mass selection.kiln drying / bow / crook / twist / plantation Résumé -Effets du procédé de séchage sur le gauchissement du bois d'épinette blanche issu d'une plantation de 36 ans.Le bois produit et récolté dans des plantations est appelé à devenir une source d'approvisionnement de plus en plus importante pour l'industrie forestière, et ce bois est susceptible de posséder des propriétés différentes de celles du bois récolté en forêt naturelle.Ceci pourrait ainsi engendrer des ajustements coûteux aux processus manufacturiers pour maintenir la qualité des produits finis.Au Canada, l'épinette blanche (Picea glauca [Moench] Voss) est une des principales essences forestières utilisées pour le reboisement, et pour la production de bois de sciage.Dans cette étude, nous avons examiné la variation génétique du gauchissement après séchage de 25 provenances de bois de colombage ainsi qu'un échantillon de forêt de seconde venue.Toutes les provenances provenaient de la région des Grands-Lacs et du Saint-Laurent.Deux traitements de séchage ont été testés, c'est-à-dire le séchage conventionnel et le séchage à haute température.Nous n'avons pas trouvé de différences significatives entre les deux traitements, non plus entre les provenances pour la voilure, la cambrure ou la torsion.Des différences significatives ont toutefois été trouvées entre la moyenne des provenances (plantation) et celle des échantillons recueillis dans une forêt de seconde venue, et ce pour la cambrure et la torsion.L'importance de la variation existant d'un arbre à l'autre laisse présager la possibilité de réaliser des gains génétiques via la sélection massique.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".