Carbon and biomass models for five Sierra Nevada mixed conifer species
Bibliographic record
Abstract
Data from tree cores and disks were used to develop biomass and carbon mass taper models for five major Sierra Nevada conifer species. These taper models were used to predict masses of tree boles, tree bole portions, branch, and foliage using carbon fraction data for oven-dried and living tissues. Taper models developed using core data were well modeled to disk data with R2 values ranging from 0.98 to 0.99 by inclusion of a calibration parameter. The fit of the final models suggests our approach can be used to include large diameter trees that cannot be cut down in biomass data collection efforts that otherwise would only sample smaller diameter trees. Our results show that biomass-weighted living carbon estimates at the whole tree level ranged from 2.8% to 9% higher than estimation methods using the standard carbon fraction of 0.5 depending on the tree species. Our approach addresses the need to account for variation in carbon fraction and wood density throughout trees, as well as demonstrating a data collection and modeling approach to include large old growth trees that cannot be destructively sampled.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".