EsPaCe: a growth model for balsam fir stands following precommercial thinning in Québec, Canada
Bibliographic record
Abstract
Precommercial thinning reduces the density of young, high-density stands, promoting the growth of selected trees. However, existing growth models are calibrated for merchantable-sized trees and do not account for saplings, which limits their ability to simulate the changes induced by precommercial thinning. To address this gap, we developed EsPaCe, a growth simulator tailored to balsam fir stands treated with precommercial thinning. Using data from 329 plots monitored over 20 years post-treatment, we calibrated six interconnected models to predict stem density, species composition, and diameter distribution at 5-year intervals. Simulation results in the balsam fir–paper birch domain show that balsam fir ( Abies balsamea (L.) Mill.) remains largely dominant after treatment. Initial stand conditions (density, composition, and quadratic mean diameter) had little effect on the final stand composition. However, later interventions in stands with higher initial quadratic mean diameters appeared to promote tree diameter growth and provide slight control over species composition. Model predictions remained unbiased over 20 years, and simulations extended to 35 years produced plausible outcomes. EsPaCe provides forest managers with a valuable tool for planning silvicultural treatments and integrates seamlessly with long-term growth models for merchantable stands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".