Classification and regression tree based survival analysis in oak-dominated forests of Missouri's Ozark highlands
Bibliographic record
Abstract
Tree survival or mortality is a stochastic process and highly variable over time and space. Many factors contribute to this process, including tree age, tree size, competition, drought, insects, and diseases. Traditional parametric approaches to modeling tree survival or mortality are often unable to capture this variation, especially in natural, mixed-species forests. We analyzed tree survival in Missouri Ozark oak forests using a combination of classification and regression tree (CART) and survival analysis of more than 35 000 trees with DBH >11 cm measured four times between 1992 and 2002. We employed a log-rank test with CART to classify trees into seven disjoint survival groups and used a nonparametric KaplanMeier (product limit) method to estimate tree survival rate and construct confidence intervals for each survival group. We found that tree species, crown class, DBH, and basal area of larger trees were the variables most closely associated with differences in tree survival rates. In these mature oak forests, mortality for the red oak species group was three to six times greater than for the white oak, hickory, or shortleaf pine species group. The results provide practical information to guide development of silvicultural prescriptions to reduce losses to mortality.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".