A landscape level analysis of yellow-cedar decline in coastal British Columbia
Bibliographic record
Abstract
Yellow-cedar (Chamaecyparis nootkatensis) is currently undergoing a dramatic decline in western North America, with concentrated areas of decline located in southeast Alaska and coastal British Columbia. Recent research suggests that a shift in climate is responsible for the decline and a working hypothesis concerning the role of climate and site specific factors has been proposed. The main objective of this research was to contribute to the understanding of the yellow-cedar decline phenomenon by examining the spatial pattern of the decline and assessing the relations with topographic variables in coastal British Columbia. The research questions were addressed through a combination of remote sensing and Geographic Information System (GIS) techniques. Sample points were distributed across the landscape according to a stratified sampling scheme and the presence/absence of decline at each point was determined using a forest cover dataset and aerial photograph interpretation. Spatial patterns of topographic factors (e.g. elevation, slope, aspect) were derived from a 25 m digital elevation model of the province. To assess the strength of relations between the distribution of decline and the various environmental predictors, logistic regression and decision-tree models were applied. The lasso technique was used to select a significant set of coefficients and the selection was then validated through bootstrap analysis. Model results indicated that low elevation sites close to the coast, which are more exposed and have more variation in elevation, are more likely to show evidence of decline. The logistic model fit the data well (Nagelkerke R² = 0.846, Hosmer-Le Cessie omnibus test failed to find any evidence of lack of fit) and had high predictive accuracy (AUC = 0.98). The topographic variables identified by the model influence degree of soil saturation, temperatures and snowpack presence in a forest stand, supporting the proposed associations in the current decline hypothesis. The analysis also highlighted the utility of the lasso logistic model for selecting significant variables and mapping high risk areas for decline. Knowledge of the determinants of the spatial pattern of decline will improve predictability and provide critical information for the conservation and management of yellow-cedar.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.090 | 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".