Differences in crown characteristics between black (<i>Picea mariana</i>) and white spruce (<i>Picea glauca</i>)
Bibliographic record
Abstract
Black spruce ( Picea mariana (Mill.) Britton, Sterns & Poggenb.) and white spruce ( Picea glauca (Moench) Voss) are phylogenetically proximal species that differ in productivity. Crown characteristics of these two species have not been extensively studied, in spite of the importance of these two species to the Canadian boreal forest and the importance of tree crowns for understanding and modelling tree growth. In this paper, we characterize and compare the crown lengths, crown profiles (i.e., radii), shapes, and surface areas of these two species using 65 white spruce and 57 black spruce trees destructively sampled in the provinces of Alberta, Ontario, and Quebec, Canada. Crown length was measured on every sample tree, while crown profile was obtained by reconstructing crowns from branch measurements. Our results showed that crown lengths did not differ between these two species given the same tree size and growth conditions. However, these two species establish under different growth conditions resulting in crown length differences. Further, differences in crown radii and profiles were found even under the same growth conditions. White spruce trees had wider crown radii and profiles changed from a parabola to a cone shape under increased density. As a result, differences in crown surface areas were found.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".