Predicting Functional Role and Occurrence of Whitebark Pine ( <i>Pinus albicaulis</i> ) at Alpine Treelines: Model Accuracy and Variable Importance
Bibliographic record
Abstract
At some alpine treelines in the Rocky Mountains, whitebark pine (Pinus albicaulis)—a keystone species—plays a central role in tree island development through facilitation. Whitebark pine occurs both as a solitary tree and also as a component of tree islands, although relative importance of these two patterns varies geographically. We examine the utility of four predictive models to understand how the functional role of a keystone species varies spatially with biophysical conditions. We use a novel data set to predict whitebark pine's functional role, characterized by spatial association and relative position within a tree island at three North American Rocky Mountain treelines. For the study areas combined, and at a study area level, we compared prediction accuracy and variable importance among these modeling approaches: general linear models, classification and regression trees, random forests, and support vector machines. Results revealed that the keystone role of whitebark pine varied spatially. For the combined model, growing season temperature and slope curvature were the most important predictive variables for association and relative position, as revealed by overall agreement among the four models. Prediction accuracy and variable importance varied at the study area level, though, indicating that different conclusions could be drawn from each model, if examined independently. We advocate comparing results from different modeling approaches for complex, field-derived data sets because it might enable a better understanding of model and variable selection and appropriateness of input data resolution. Furthermore, comparative modeling enables assessment of the relative predictive and interpretive capacities of each modeling approach.
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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 | 0.001 | 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 | 0.000 |
| 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 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".