Testing the significance of different tree spatial distribution patterns based on the uniform angle index
Bibliographic record
Abstract
The uniform angle index (UAI) is used to characterize the spatial distribution of a forest community or of individual tree species within that community. This study presents an empirical assessment of the performance of a new statistical test used in conjunction with the UAI. In this study, the UAI is applied to observed and simulated communities. The effect of plot size and density are examined and the results are compared with the criteria of the aggregation index R of Clark and Evan (CE-R) and Ripley’s L function (RL). The results suggest that the UAI performs well in determining a particular class of spatial pattern and that, in contrast to the CE-R index, it is unaffected by population density because the standard deviation can be estimated when the density is known using a new empirical relationship presented in this paper. The results of this study represent an improvement in the development of the UAI, verifying its performance and confirming its overall utility. Our comparative analysis positions the UAI among its historical competitors, the CE-R and RL criteria. The UAI index, which does not require expensive mapping of tree positions, can produce results that are equivalent to those obtained by RL and more robust and reliable than those obtained by CE-R.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".