Biomass partitioning in a miniature-scale loblolly pine spacing trial
Bibliographic record
Abstract
Stand conditions influence the partitioning of biomass to stem, needle, branch, and root components. Using data from 4- to 6-year-old loblolly pine ( Pinus taeda L.) trees grown in a miniature-scale spacing trial, this study determined the effect of initial spacing on the biomass partitioning of loblolly pine. Multivariate analysis of variance procedures concluded that row and column spacing did not have a significant effect on the relative amount of biomass among tree components. Root/shoot and height/diameter ratios, however, differed across densities, indicating that allometric-based partitioning tradeoffs occurred. Results from the miniature-scale trees showed trends similar to those observed with mature-sized trees at operational spatial scales. Stem and woody roots were 70% and 14% of total mass, respectively. Since these trees were physiologically young at the time of harvest, the allocation of mass to needle continued to be a priority, accounting for 10% of the total mass. Initial planting spacing did not directly affect partitioning patterns; however, allometric ratios offered some evidence that partitioning may have changed between above- and below-ground tree components. This analysis offers insight into using principles from similarity analysis to analytically relate biomass partitioning from miniature to operational spatial scales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".