Mapping site indices for five Pacific Northwest conifers using a physiologically based model
Bibliographic record
Abstract
Questions: How well can we predict tree growth potential (site index) of five, locally dominant tree species in reference to estimates made with a detailed vegetation classification? Location: The forested region of the Pacific Northwest, USA and Canada. Methods: We employed a physiologically based process model (3-PG, Physiological Processes to Predict Growth) to generate estimates of site index under averaged climatic conditions (1971–2000) generated from hundreds of weather stations and extrapolated, with adjustments for topography, across the region at 1-km resolution. The model was parameterized from published information, but we had to assume fixed values of soil water storage capacity at 200 mm and soil fertility at 70% of maximum across the region. Field estimates of site index for the five dominant species were derived from published correlations with detailed mapping of vegetation provided by The British Columbia Ministry of Forests and Range. Results: The site indices projected with the 3-PG model for the five species combined, when compared with those produced by the Ministry of Forests and Range, produced an r2 averaging ∼0.5 with a standard error of 2.8 m at 50 yr, equivalent to 10% of the mean. Some of the variation may be attributed to inadequate information on soil properties. Importantly, the relationship between the two estimates was not significantly different from a 1:1 line, with an intercept of zero. Conclusions: The 3-PG modelling approach offers a means of predicting spatial variation in site indices across the Pacific Northwest and provides a basis for predicting future site indices under a changing climate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".