How do tree- and stand-level factors influence belowground biomass and carbon storage in tanoak (<i>Notholithocarpus densiflorus</i>)?
Bibliographic record
Abstract
Tanoak (Notholithocarpus densiflorus) is the most common hardwood in northern California forests, yet its capacity for belowground carbon storage is unknown. To study relationships between coarse roots and tree and stand variables, we destructively sampled twelve tanoak root systems in Humboldt County, California. To estimate belowground biomass, we summed measured biomass of the root ball and a subsample of lateral roots along with predicted biomass of unmeasured coarse roots. Tree size was the best linear predictor of belowground biomass and carbon, indicating that a 25-cm diameter tanoak, for example, stored 70 kg of biomass and 34 kg of carbon in its root system. Stand density was also influential: a doubling of stand density index reduced belowground carbon by 22% for the average tanoak. The mean root-toshoot ratio of 0.35 varied between 0.11 and 0.65, with larger tanoak at high stand densities allocating proportionally less biomass belowground than small open-grown tanoak. The findings highlight the importance of accounting for stand density effects, otherwise belowground carbon will be under predicted in low-density stands managed for tree health, vigor, and resistance to drought and wildfire, or overestimated in forests managed at high densities for high carbon sequestration.
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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 | 0.001 |
| 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 | 0.001 | 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".