A spatial analysis of fine-root biomass from stand data in the Pacific Northwest
Bibliographic record
Abstract
High spatial variability of fine roots in natural forest stands makes accurate estimates of stand-level fine-root biomass difficult and expensive to obtain by standard coring methods. This study uses aboveground tree metrics and spatial relationships to improve core-based estimates of stand-level fine-root biomass. Using the multiple-tree Ribbens model for pure stands, the approach assumes that the total fine-root biomass at a given point is the additive contribution of the nearest dominant trees and that fine-root biomass for a single tree depends on the distance to the trunk and its size. A Monte Carlo random sampling technique, or sampling on a regular grid, is used to estimate the average fine-root biomass across the stand. We illustrate the applicability of this approach by using it on root-core data from a Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stand and a western juniper (Juniperus occidentalis Hook.) stand in the Pacific Northwest. We conclude that stand-level fine-root biomass is adequately estimated using the Ribbens model. Unlike the model-based estimate for stand-level fine-root biomass, the accuracy and precision of the arithmetic mean of the coring samples depends on the spatial heterogeneity of root distributions and the representativeness of the root coring samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".