Modelling sapling density for sugar maple-dominated mixed stands of eastern Canada
Bibliographic record
Abstract
Saplings (tree species whose diameter at breast height ranges from 1.1 to 9.0 cm) are important in forest growth and development. Their abundance and density can lead to specific forest successional trajectories and ecosystem characteristics. Yet, the consideration of saplings in forest management planning is a relatively new topic and is still rarely included in forest growth models. We developed sapling density models for 10 species groups with the objective of providing forest managers with additional tools to support the development of more precise prediction systems. We used dendrometric and environmental variables to model sapling density and species-wise density ratios. We evaluated Poisson and gamma regressions for the modelling of sapling density and zero-inflated logistic regressions to model species ratios. We used repeated measurements (from 1982 to 2019) from permanent sample plots located in hardwood forests in northeastern North America. Our results show that the gamma regression was superior to the Poisson regression. The cross-validated mean bias using gamma regression was 55 stems·ha−1 with a relative percentage error of 2.5% and an R2 of 0.43. The species-wise sapling density ratio model had an overall R2 of 0.93, and the species-wise mean R2 ranged between 0.90 and 0.96. Among the examined model covariates, stem density of merchantable-sized trees and latitude were significant in both models. We believe that the models we developed can be useful for forest management planning and sustainable merchantable production.
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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".