Spatial pattern of central African rainforests can be predicted from average tree size
Bibliographic record
Abstract
When considering all trees irrespective of their species, natural tropical rain forests typically exhibit spatial patterns that range from random to regular. The regularity is often interpreted as a footprint of tree competition. Using 23 permanent sample plots totalling 61 ha in the rain forests of central Africa, we characterized their spatial patterns and modelled those that exhibited regularity by a Strauss point process. This Strauss process is obtained as a Markov point process whose interaction function is an exponential function of a competition index commonly used in forestry. The parameter of this Strauss process characterizes the strength of competition. The 23 plots in central Africa differed in tree density and basal area, and could be discriminated depending on the type of spatial patterns: plots having a large basal area with respect to their density had a non regular pattern, whereas those having a small basal area with respect to their density had a regular pattern. For those plots that exhibited regularity, average tree size could be used to predict the strength of competition. The parameter of the Strauss process was significantly related to the average size by a linear relationship, such that competition decreases as average tree size increases. This relationship extrapolated to a null value of the Strauss parameter when average tree size reaches 32 cm in diameter. This relationship between average tree size and spatial pattern is a testable feature for future studies on the relationship between competition and spatial pattern in natural forests.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".