Modelling a mesoscale forest: Can a regional growth model be applied to manage a small landscape?
Bibliographic record
Abstract
Forest models can be developed from empirical relationships between stand attributes, including age, and yield. Empirical growth and yield (G&Y) models remain popular with forest managers for their simplicity and utility. It is important to apply models at the spatial levels at which they were developed to avoid the fallacy of disaggregation. This study attempted to determine whether regionally aggregated G&Y curves perform adequately when applied at the sub-regional scale, and if not, if their application can be modified to better model a mesoscale forest. We studied a mesoscale forested watershed in Nova Scotia, Canada, to determine if regional G&Y curves could predict stand merchantable volume (MV) using data available from a photogrammetric provincial forest inventory for initial stand conditions. Initial results demonstrated that curves significantly underestimated stand MV compared to 700 forest cruise point observations throughout the study area. Curves were reassigned based on observations and local knowledge, and subsequently generated estimates of stand MV not significantly different from observations. We found that while regional growth models are not ideal for mesoscale application, when properly calibrated through field observations, they can offer insights into current conditions and the future potential of a forest with minimal additional data collection required.
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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 | 0.002 |
| 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 | 0.001 |
| 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".