Modelling the change in aspen species composition in boreal mixedwoods
Bibliographic record
Abstract
The dynamics of aspen (Populus tremuloides Michx.) species composition (SC, defined as the ratio of aspen basal area to total stand basal area) in boreal mixedwoods were modelled in this study using the difference equation method, based on datasets collected from repeatedly measured permanent sample plots (PSPs) across Alberta. The aspen SC measured at time 1 was taken as a key predictor variable to project SC values at future ages. Since site quality was found to impact the aspen SC, site index (SI) was incorporated into the model. To correct the autocorrelation and heteroskedasticity problems associated with PSPs data, the non-linear mixed-model technique was applied to accommodate the variance–covariance structure of the error terms and to estimate model parameters. The final model was evaluated on an independent dataset collected from a different region in Alberta, based on a number of statistical measures including a goodness of prediction index (GOPI) from forward and backward projections, and examinations of residual and studentized residual plots. The low prediction bias and high GOPI value obtained from forward and backward projections on the validation data suggest that the model fitted the data well and can be reliably applied to predict changes in aspen SC in boreal mixedwoods across Alberta. The model showed a decline in the SC after year 30, but the decline was steeper in sites of low SI. The model can be used for modelling the transitions of forest compositions in boreal mixedwood forests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".