Effects of turnover and internal variability of tree root systems on modelling coarse root architecture: comparing simulations for young <i>Populus deltoides</i> with field data
Bibliographic record
Abstract
The behaviour of the FracRoot model, which describes coarse-root architecture based on proximal root diameters using a recursive algorithm, was studied using field data on Populus deltoides Bartr. ex Marsh. in a short-rotation plantation. Roots were described as a branching, hierarchical network of connected links. Diameters of daughter links were estimated from the diameter of the mother link using scaling parameter p and allocation parameter q, which were based on sharing of the root cross-sectional area by daughter links. Parameters were estimated from complete root excavations. The length of each link and the vertical and horizontal branching angle distributions were recorded. Parameter p values were distributed log-normally, and q values followed a beta distribution both for the whole root system and within 5 mm link diameter classes. Including the variability of p and q in the model did not significantly improve root length estimates compared with the use of mean p and q values over all branching points. Including a coarse-root turnover factor based on field-observed evidence on root mortality improved the model fit to field data. Root length was more sensitive to parameter values and turnover factor than root mass. Field observations and the importance of the turnover factor to simulation accuracy suggest that coarse-root turnover should be considered in root research, at least under conditions of strong competition or other external stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".