Analyzing risk of regeneration failure in the managed boreal forest of northwestern Quebec
Bibliographic record
Abstract
Changes in the fire regime can affect the postdisturbance regeneration potential of boreal forest tree species, thereby modifying tree density and cover. This could adversely affect the sustainability of forest management, especially in regions currently characterized by a short fire cycle and low productivity. As a case study, we use a real landscape (1.3 Mha) in the boreal forest of northwestern Quebec, characterized by a high annual area burned and where fire activity is projected to strongly increase, to model the effect of current (baseline) and climate-induced (projected) changes in the fire cycle and harvesting rate on the regeneration failure potential of pure black spruce (Picea mariana (Mill.) BSP) and jack pine (Pinus banksiana Lamb.) stands. Simulations were carried out over a 50-year period under three reproductive maturity thresholds per species, representing the age at which an adequate seed supply is attained to ensure self-replacement. Results show a progressive increase in the area affected by natural regeneration failure over the course of the simulation period under both climate scenarios, culminating with an 18.5% loss (149 210 ha) of productive area under the baseline scenario and a 65.8% loss (532 141 ha) under the projected scenario (intermediate maturity threshold and current harvest rate). Variation in the fire cycle had the greatest effect on the regeneration failure rate, followed by regeneration threshold age and harvest rate. We outline proactive forest management practices to reduce the likelihood of regeneration failure following fire. This includes intensive stand management and retention strategies following timber harvest. Monitoring of forest recovery after fire would help in assessment of regeneration failure over time and be useful in validating both model results and the efficacy of strategies aimed at minimizing its likelihood.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".