EXPLORATION OF THE PHYSICAL PROPERTIES OF INTERNAL CHARACTERISTICS OF SUGAR MAPLE LOGS AND RELATIONSHIPS WITH CT IMAGES
Notice bibliographique
Résumé
Two groups of sugar maple (Acer saccharum Marsh) logs were scanned using an X-ray scanner to identify and locate their main internal characteristics. In the design of this exploratory study, five logs produced from a freshly cut tree (Group 1) and three logs (Group 2) sampled from a sawmill yard, were scanned to identify the various wood types present: rot; knots; colored heartwood; and sapwood. Based on these computed tomography (CT) images, four or five disks (20 mm thick) were cut from each log. Blocks of 10 x 10 x 10 mm were then cut from each disk representing areas of each type of wood. The green density, basic density, and moisture content of each block were measured to assess within-tree variations between logs and within wood type. CT grey levels in the CT images were then measured for individual blocks to assess the feasibility of identifying wood types. Finally, a correlation analysis was carried out between physical properties and CT grey levels for each internal characteristic. The results generally indicated that, for both groups of logs, the type of wood was the most significant source of variation in green density, basic density, and moisture content. Statistically significant differences in these physical properties were also observed between sapwood and the other types of wood. This is a result of practical importance since sapwood in sugar maple is the main driver of product value. From the grey level variation observed in CT images, it is possible to separate sapwood from colored heartwood and knots. The contrast in grey level between the sapwood and the area of rot is not so obvious, but it can be enhanced by means of statistical methods. The correlation analysis indicated that green density was the variable that best correlated with grey level variations in CT images. A linear relationship between green density and grey level was established for each type of wood. The coefficient of determination (R2) obtained from the simple regression analysis between grey level and green density varied between 0.32 and 0.82 depending on wood type. Nevertheless, significant differences were observed in the slopes of the curves. It is hypothesized that these differences could be mainly attributed to differences in the content and orientation of crystalline structures present in each type of wood.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».