Quantitative, nondestructive estimates of coarse root biomass in a temperate pine forest using 3‐D ground‐penetrating radar (GPR)
Bibliographic record
Abstract
Abstract Coarse root biomass was estimated in a temperate pine forest using high‐resolution (1 GHz) 3‐D ground‐penetrating radar (GPR). GPR survey grids were acquired across a 400 m2 area with varying line spacing (12.5 and 25 cm). Root volume and biomass were estimated directly from the 3‐D radar volume by using isometric surfaces calculated with the marching cubes algorithm. Empirical relations between GPR reflection amplitude and root diameter were determined for 14 root segments (0.1–10 cm diameter) reburied in a 6 m2 experimental test plot and surveyed at 5–25 cm line spacing under dry and wet soil conditions. Reburied roots >1.4 cm diameter were detectable as continuous root structures with 5 cm line separation. Reflection amplitudes were strongly controlled by soil moisture and decreased by ~40% with a twofold increase in soil moisture. GPR line intervals of 12.5 and 25 cm produced discontinuous mapping of roots, and GPR coarse root biomass estimates (0.92 kgC m−2) were lower than those obtained previously with a site‐specific allometric equation due to nondetection of vertical roots and roots <1.5 cm diameter. The results show that coarse root volume and biomass can be estimated directly from interpolated 3‐D GPR volumes by using a marching cubes approach, but mapping of roots as continuous structures requires high inline sampling and line density (<5 cm). The results demonstrate that 3‐D GPR is viable approach for estimating belowground carbon and for mapping tree root architecture. This methodology can be applied more broadly in other disciplines (e.g., archaeology and civil engineering) for imaging buried structures.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".