Improved Branch Volume Prediction of Multi-Stemmed Shrubs: Implications in Shrub Volume Inventory and Fuel Characterization
Bibliographic record
Abstract
Accurately estimating the volume of woody vegetation is critical for assessing fuel characteristics and associated wildfire risks in shrublands. However, few studies have investigated the branch volume of multi-stemmed shrubs, a dominant life form in wildfire-prone drylands. This study predicts branch volume using the inflection point of branch diameter. This inflection point, identified using the “Segmented” package in R, marks the transition from a gradual decrease to a significant reduction in diameter along the stem. The volume of branch segment above this point is calculated as a cone, and below it, a cylinder. We validated this method on various species such as Caragana korshinskii, Salix psammophila, and Vitex negundo. Good estimations were achieved with an average 19.2% bias relative to reference branch volumes, outperforming conventional methods that subjectively treated the whole branch as either a cylinder (96.9% bias) or a cone (−34.4% bias). We tallied branches by basal diameter and provided inventories for easily locating the inflection point, as well as using two-way branch volume tables for rapid volume predictions in shrubland. In general, we developed an effective method for estimating branch volumes of multi-stemmed shrubs, enabling its application to larger-scale shrubland volumetric prediction. This advancement supports wildfire hazard assessment and informs decision-making in fuel treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".