Point process modelling of root distribution in pure stands of <i>Fagus sylvatica</i> and <i>Picea abies</i>
Bibliographic record
Abstract
A previous study by Schmid and Kazda (I. Schmid and M. Kazda. 2001. Can. J. For. Res. 31: 539548) evaluated the vertical distribution and radial growth of coarse roots greater than 2 mm diameter in pure and mixed stands of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.). The vertical distribution of roots of Norway spruce was fitted by an exponential function, while the root distribution of European beech was approximated by a gamma distribution. Now, in the present paper, planar point process models have been applied to investigate the spatial (two-dimensional) distribution of data for roots between 2 and 5 mm diameter. After a homogenization with respect to the vertical axis, the pair correlation function and the L function were estimated to fit Matérn-cluster point process models to the given root data. The models were finally vertically retransformed to provide information on the inhomogeneous spatial patterns of small roots as well as on the original shape and size of the root clusters. All models based on vertically transformed data confirmed that the root distribution patterns are not completely random, as they indicated root clustering for both species, with different degrees of exploitation intensity (clustering) between the two species. According to the Matérn-cluster models, Norway spruce had stronger clustering in smaller cluster regions, while roots of European beech formed weaker clusters in larger cluster regions. Furthermore, beech root clusters seemed to avoid overlapping. Together with previous studies on the root system of both species, the present study indicates more intensive belowground intraspecific competition for spruce than for beech. On the other hand, the clustering characteristics described indicate that European beech has a more sophisticated rooting system than Norway spruce. The spatial distribution of the inhomogeneous raw data is characterized by the clustering properties analysed in the present paper and by the vertical distribution previously studied.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".