Evaluating a single tree-based growth model for even-aged stands against the maximum size–density relationship: Some insights from balsam fir stands in Quebec, Canada
Bibliographic record
Abstract
In this study, we addressed the issue of model evaluation when long-term monitoring data are unavailable or inappropriate. More specifically, we fitted a single tree-based growth model for pure even-aged balsam fir stands and we compared stochastic predictions with an existing maximum size–density relationship (MSDR). Growth trajectories for plots of different initial densities and diameter distributions were simulated over a 70-year period using 500 realizations for each combination of initial density-diameter distribution. Long-term predictions were consistent with the existing MSDR. The model properly reproduced the senescence phase in which the trajectories diverge from the MSDR. This phase was initiated when the average tree volume reached 0.2-0.3 m3 per tree, which roughly corresponded to a DBH (diameter at breast height, 1.3 m from the ground) between 19 and 23 cm. Although it cannot be generalized, our case study shows that a simple single tree-based growth model with a distance-independent competition index and no stand density index can reproduce an existing MSDR. The match between long-term predictions and an existing MSDR strengthens the confidence in the biological behaviour of the model.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".