Quantifying Stem Quality Characteristics in Relation to Initial Spacing and Modeling Their Relationship with Tree Characteristics in Black Spruce (Picea mariana)
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Bibliographic record
Abstract
Abstract This study evaluated and quantified the relationship of tree growth and stem quality characteristics with initial spacing in black spruce (Picea mariana). The study was based on the oldest and mature black spruce initial spacing trial established in 1950 in Thunder Bay, Ontario, Canada. Results show that initial spacing had a direct effect on tree and stem quality characteristics of individual trees. With increasing initial spacing from 1.8 × 1.8 m to 2.7 × 2.7 m, diameter at breast height (dbh), crown size, tree height, tree volume, tree taper, branch size, and clear log length show a steady increase, but the three initial spacings of 2.2 × 2.2 m, 2.0 × 2.0 m, and 1.8 × 1.8 m show no significant differences in these stem characteristics. The results suggest that black spruce may have a low capacity of responding to spacing unless spaced to 2.7 × 2.7 m. Multiple comparison tests support the results with the exception for crown width. This suggests that crown width may be the best density-size indicator for black spruce. As far as tree growth, stem quality, and initial establishment costs are concerned, this study suggests that an initial spacing such as 2.2 × 2.2 m might be optimum for wood production. In addition, this study shows that wide spacings (e.g., 2.7 × 2.7 m) can result in a significant decrease in stem quality and thus may have significant implications for product quality. Furthermore, relationships between major stem quality parameters and tree and stand-level characteristics were examined. Results of the stepwise regression analysis strongly indicate that crown width is important in determining stem quality. The stem quality parameters all have a high adjusted r2 and a low standard error of estimate when regression is made on tree or stand characteristics using the stepwise regression method.North. J. Appl. For. North. J. Appl. For. 22(2):85–93.
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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 | 0.001 | 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 | 0.000 | 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 it