A landscape-level analysis of yellow-cedar decline in coastal British Columbia
Bibliographic record
Abstract
Yellow-cedar (Chamaecyparis nootkatensis D. Don (Spach)) is currently undergoing a dramatic decline in western North America. Recent research suggests that site factors combined with a shift in climate have predisposed yellow-cedar trees to decline. We conducted the first landscape-level analysis of the decline in coastal British Columbia to assess relations between the decline and topographic variables. We used lasso-penalized logistic regression to model yellow-cedar decline presence and absence with topographic variables derived from a digital elevation model. 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) 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 areas at high risk for decline. Knowledge of the determinants of the spatial pattern of decline will improve predictability and provide critical information for conservation and management of yellow-cedar.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".