Impact du charançon du pin blanc (<i>Pissodes strobi</i>Peck) dans les plantations dépinette de Norvège (<i>Picea abies</i>(L.) Karst.). Partie 2 : Propriétés du bois des sciages
Bibliographic record
Abstract
We studied the lumber characteristics of 148 trees taken for that purpose during commercial thinning operations in three Norway spruce (Picea abies [L.] Karst.) plantations, aged from 32 to 34 years, which had been attacked by the white pine weevil (Pissodes strobi [Peck]). The trees, with diameters ranging from 14 to 23 cm, were grouped into three quality classes according to the number of major deformations caused by the weevil. The effects of the deformations on rigidity, bending strength, wood density and lumber defects were examined. Results show that in general the deformations caused by the weevil do not affect the lumber properties of Norway spruce on sites of medium- to high-quality. For the three Norway spruce plantations, the mean values of the modulus of elasticity (MOE) of the lumber vary from 8510 to 9357 MPa, and for the modulus of rupture (MOR) range from 36.0 to 42.5 MPa, whereas wood density varies from 324 to 343 kg·m -3 . For comparison purposes, the same measurements were taken on lumber from 38 trees in a white spruce (P. glauca [Moench] Voss) plantation that was unaffected by the weevil, but otherwise comparable to the Norway spruce plantation on one of the sites studied. The lumber properties from the Norway spruce taken from deformed or undeformed stems are 34% superior to the white spruce for the MOE, 20% for the MOR and 8% for wood density. The smaller knots in the Norway spruce could explain this difference. On the other hand, a visual classification of this type of wood reveals few differences in the lumber's bending strength or rigidity. Key words: Norway spruce, Picea abies, wood characteristics, MOE, MOR, wood density, lumber, white pine weevil, Pissodes strobi, white spruce, Picea glauca
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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 | 0.000 | 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 | 0.001 | 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 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".