The relationship between site and tree characteristics and the presence of wet heartwood in black spruce in the boreal forest of Quebec, Canada
Bibliographic record
Abstract
Wet heartwood in black spruce (Picea mariana (Mill.) BSP) causes considerable problems during the drying process. Forest companies try to avoid harvesting stands with wet heartwood, but no relationship has been yet established between the incidence of wet heartwood and tree or site characteristics. To characterize areas containing a significant proportion of black spruce affected by wet heartwood, a total of 635 black spruce trees were sampled in eighteen 400 m2 study plots under management in the central boreal forest of Quebec. A total of 18 study sites were analysed and classified as wet, intermediate, or dry, based on the proportion of individuals with wet heartwood. Thirteen of the study sites were classified as wet, two as intermediate, and three as dry. The average age calculated for trees on wet sites was significantly (p = 0.0001) higher than that of the other two classes, whereas growth rate was significantly lower on wet sites. No difference was noted in the average height or diameter of the individuals from all three classes. The wet sites contained organic soil, whereas Podzols characterized two of the three dry study sites. An additional sampling of black spruce (n = 509) revealed a significant relationship between the groundwater level and heartwood moisture content classification (i.e., dry, intermediate, or wet). Trees in the dry heartwood class grew on sites with the lowest groundwater levels (p = 0.002) compared with trees in the wet or intermediate classes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".