Modelling Forest Succession Under A Harvest Regime In Mixed Stands In Quebec, Canada
Bibliographic record
Abstract
Understanding forest regeneration over large-scale and under harvesting regime is essential for natural resource management. In this study, we used a dataset from a network of monitoring plots in the regions of Bas-Saint-Laurent and Gaspu00e9sie, in Quebec, Canada, as a real-world case study. Based on statistical models, we combined different techniques into an approach that predicts forest succession under harvesting. Firstly, we fitted a binomial model to predict regeneration after harvesting. Once a regeneration was predicted, a gamma model was fitted to estimate the stocking. Forest composition before and after harvest, as well as climatic and topographic variables, were used as explanatory variables. The models were evaluated and validated, and the results showed the potential of the approach to provide predictions of forest succession in the area of study. We observed that topography strongly influenced regeneration and stocking. The results also revealed the temporal evolution of post-harvest forest composition in terms of stocking. This modeling approach should overcome actual challenges reported by managers in the study regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.041 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.030 |
| Open science | 0.015 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; both teacher heads agree on what is shown here.
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".