Using JABOWA-3 for forest growth and yield predictions under diverse forest conditions of Nova Scotia, Canada
Bibliographic record
Abstract
Empirical growth and yield models developed from historical data are commonly used in developing long-term strategic forest management plans. Use of these models rests on an assumption that there will be no future change in the tree growing environment. However, major impacts on forest growing conditions are expected to occur with climate change. As a result, there is a pressing need for tools capable of incorporating outcomes of climate change in their predictions of forest growth and yield. Process-based models have this capability and may, therefore, help to satisfy this requirement. In this paper, we evaluate the suitability of an ecological, individual-tree-based model (JABOWA-3) in generating forest growth and yield projections for diverse forest conditions across Nova Scotia, Canada. Model prediction accuracy was analyzed statistically by comparing modelled with observed basal area and merchantable volume changes for 35 permanent sample plots (PSPs) measured over periods of at least 25 years. Generally, modelled basal area and merchantable volume agreed fairly well with observed data, yielding coefficients of determination (r2) of 0.97 and 0.94 and model efficiencies (ME) of 0.96 and 0.93, respectively. A Chi-square test was performed to assess model accuracy with respect to changes in species composition. We found that 83% of species-growth trajectories based on measured basal area were adequately modelled with JABOWA-3 (P > 0.9). Model-prediction accuracy, however, was substantially reduced for those PSPs altered by some level of disturbance. In general, JABOWA-3 is much better at providing forest yield predictions, subject to the availability of suitable climatic and soil information.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".