Variation in occurrence and extent of internal stem decay in standing trees across the eastern US and Canada: evaluation of alternative modelling approaches and influential factors
Bibliographic record
Abstract
The occurrence (probability) and extent (proportion) of tree internal stem decay are important attributes influencing potential wood quality and value, but variation in decay by species, tree size and geographic range are rarely evaluated and modelled. In this analysis, we used 1246 destructively sampled trees across 33 species in the northeastern United States and New Brunswick, Canada to determine the factors influencing the combined probability and proportion of decay. In the process, we evaluated three modelling approaches including a two-part conditional model, multinomial model and generalized additive model for location, scale and shape (gamlss) that simultaneously predicted both probability and proportion of decay. Predictive capability for all three methods were nearly identical when classifying decay occurrence. Compared with the other methods, the gamlss model had a lower mean bias and root mean square error (RMSE) when predicting decay extent. Tree diameter to height ratio (ratio of diameter at breast height to total height), height, crown ratio, species tolerance to flooding and drought, leaf longevity, and an assessment of perceived tree risk of mortality (risk class) were selected as predictors in the best overall model for decay occurrence. For predicting decay extent, the best model included risk class, crown ratio and the last freezing date of spring. Further analysis identified significant species differences, which we used to develop functional species groupings based on decay occurrence and extent. Despite these observed relationships, a high degree of unexplained variation remained, highlighting the challenges of modelling decay in trees of different species across a range of growing environments.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".