Assessing inter- and intra-specific variation in trunk carbon concentration for 32 neotropical tree species
Bibliographic record
Abstract
Trunk carbon (C) concentrations were assessed for 32 species of tropical trees to understand sources of variation. The main effect of species accounted for 38% of the total variance in C concentration (p < 0.0001). Tectona grandis demonstrated the greatest C concentration (49.4%), while Ormosia macrocalyx displayed the lowest C concentration (44.4%). We also observed significant differences among the sampling sites (F = 2.2, p < 0.02). For three of the species sampled in both plantations and natural forests, the natural forest individuals had significantly higher C concentrations (Dipteryx panamensis: F = 6.10, p = 0.06; Hura crepitans: F = 5.53, p = 0.06; and Miconia argentea: F = 8.92, p = 0.02). C concentration was highly correlated with wood specific gravity (r 2 = 0.86). A canonical correspondence analysis was performed to identify the environmental and (or) growth factors explaining variation in trunk C concentration. The two factors with the highest loading values on the first canonical axis are site and diameter at breast height (DBH), while DBH and density load on axis 2. The biplot shows that species respond differently to environmental factors. Our results suggest that a better consideration of interspecific variation in C concentration could reduce the error associated with estimates of C sequestration by up to 10%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".