Evaluating height structure in Scots pine forests using marked point processes
Why this work is in the frame
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Bibliographic record
Abstract
In this study, the second-moment analysis of marked spatial point processes is applied to the characterization of the tree height distribution in two Scots pine ( Pinus sylvestris L.) forests in the Central Mountain Range of Spain. The cumulative function L m (d) weighted by the normalized mark variance is proposed to analyse the second-order properties of marked point patterns. The empirical L m (d) was compared with two null models to assess the relationship between the spatial distribution of the trees and the tree height correlations: the first null model was used to characterize the spatial clustering of the trees and was derived from the complete spatial randomness model used with Ripley’s K(d) function. The second null model, which is derived from the random labelling model used with the intertype second-moment measure K 12 (d) (type 1 intensity conditioned to the type 2 intensity and vice versa), allows us to identify the mark correlations. The performance of the technique was assessed through simulated marked point patterns. The second-moment analysis showed that most of the analysed Scots pine stands have a uniform height distribution at small scale and greater heterogeneity at large scales, with the exception of an upper altitudinal stand, which exhibited heterogeneity at short distances. These results demonstrate the utility of second-moment analysis of marked point processes for characterizing height structure in forest stands and the interaction between the height and the spatial pattern of the trees.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it