Modelling the effects of climate on site productivity of white pine plantations
Bibliographic record
Abstract
Ninety-three dominant or co-dominant white pine (Pinus strobus L.) trees were sampled from 93 plots (one tree per plot) in even-aged monospecific plantations at 31 sites (three plots per site) across Ontario, Canada. Stem analysis data collected from these trees were used to develop and evaluate stand height models. The effects of site and climate on site productivity were examined by incorporating site and climate variables into a stand height model. Including climate variables improved the fit statistics of the stand height model for white pine. A covariance structure (AR(1)) was used to address autocorrelation in the data. Similarly, a variance function was used to account for heteroscedasticity. Stand heights were predicted for four areas (middle, easternmost, westernmost, and southernmost parts of Ontario where white pine were sampled) for the period 2021 to 2080 under two emissions trajectories known as representative concentration pathways (RCPs), with each reflecting different levels of heat at the end of the century (i.e., 2.6 and 8.5 W·m–2). At the end of the 2021 to 2080 growth period, projected heights were shorter by 7% for the southern parts and taller by 9.8% for the middle parts of Ontario under both climate change scenarios compared with those under a no change scenario. However, there was no pronounced difference in projected heights under both climate change scenarios and the no change scenario for the other two areas evaluated. The resulting height growth models can be used to estimate stand heights for white pine plantations in a changing climate. Using the same model, the site index of a plot or stand can be estimated by calculating height at a given base (index) age. In the absence of climatic data, the model fitted without climate variables can be used to estimate stand heights and site indices.
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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".