Influence of Stand Density on Ring Width and Wood Density at Different Sampling Heights in Black Spruce ( Picea Mariana (Mill.) B.S.P.)
Bibliographic record
Abstract
Thirty-six black spruce sample trees were collected from an 80-year-old stand to examine the influence of stand density on selected wood quality attributes and their variation with sampling height.The stand, naturally regenerated from fire in 1906, was located in Chibougamau, 400 km north of Québec.Each tree was assigned a local stand density ranging from 1390 to 3590 stems/ha, calculated from the number of neighboring trees.The trees were grouped into three stand density categories (1790, 2700, and 3400 stems/ha).Each sample tree was analyzed by X-ray densitometry, and various ring features including ring width and wood density were measured for each ring from pith to bark, at three heights (2.4,5.1, and 7.8m) and ring area and earlywood proportion were computed.For all features studied, the variation due to sampling height was larger than that due to stand density.The longitudinal variations for ring density and earlywood density depend largely upon the wood type (juvenile wood or mature wood).A variation of ring density with sampling height in the stem from 425 to 458 kg/m 3 was observed in juvenile wood, but variations with stand density in all the growth ring features studied were small.Notably, it was observed that stand density had more influence on ring width features than on ring density features.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 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".