Predicting lodgepole pine site index from climatic parameters in Alberta
Bibliographic record
Abstract
We sought to evaluate the impact of climatic variables on site productivity of lodgepole pine (Pinus contorta var. latifolia Engelm.) for the province of Alberta. Climatic data were obtained from the Alberta Climate Model, which is based on 30- year normals from the provincial weather station network. Mapping methods were based on ANUSPLIN, Hutchinson's thin-plate smoothing spline in four dimensions (latitude, longitude, elevation, climatic variable). Site indices based on stem analysis (observed dominant height at an index age of 50 years at breast height) were used as a measure of forest site productivity. A total of 1145 site index plots were available for lodgepole pine, the major forest species in Alberta. Regression analyses were used to predict site index as a function of climatic variables for each plot. The strongest linear predictors of site index were growing degree days > 5° (GDD5), the Julian date when GDD5 reaches 100 (D100), and July mean temperature (MTWM).A nonlinear model with D100 as the predictor variable was chosen as the final model. Both the observed and the predicted site indices from the 1145 locations were interpolated using ANUSPLIN and mapped using ArcView. We concluded that climate is an important component of site productivity, accounting for about one quarter of the variation in lodgepole pine site index across the province. Key words: site index, climate variable, Alberta Climate Model, ANUSPLIN, site productivity map, lodgepole pine
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".