Wet Heartwood Distribution in the Stem, Stump, and Root Wood of Black Spruce in the Quebec Boreal Forest, Canada
Bibliographic record
Abstract
Abstract Wet heartwood has been studied since the beginning of the 20th century. The present work focused on wet heartwood of 44 black spruces (Picea mariana (Mill.) B.S.P.) from boreal forest swampy sites Quebec, Canada. Trees were studied to characterize their tree crown shape, quickly identify them in the field, measure their moisture content, establish their moisture content distribution pattern, evaluate the wet heartwood volume inside their stem, and find the possibility of water entrance in this species. Black spruces with wet heartwood were characterized by a typical tree shape with a small number of living branches, short branches, and a clump of green needles at the top of the tree. The wet heartwood was characterized by high moisture content at the stem base and decreased with stem height. Wet heartwood was observed as high as 5 m above stem base for trees around 10.5 m in height. Moisture content of sapwood along the stem varied from 81 to 161%, whereas in dry heartwood, it was around 43% but reached more than 100% in the wet heartwood. Wet heartwood volume in the first 3.25 m of black spruce stems averaged 13% with variations between 2 and 35% per study site. Twelve stumps had wet heartwood moisture contents reaching 143% or higher. Moisture content of wet heartwood in root sections closest to the stump varied along a gradient: lower or absent at ground level and increasing with depth up to a maximum value before decreasing again. North. J. Appl. For. 22(1):12–18.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".