A stand-level tool for predicting the natural regeneration density of black spruce and jack pine following fire and salvage
Bibliographic record
Abstract
A tool was developed to allow managers and foresters to quickly assess reforestation needs following forest fire and salvage logging at the stand level in both pure and mixed black spruce and jack pine stands. This on-site operational assessment tool was created using a forest regeneration model that simulates the natural regeneration densities of black spruce and jack pine following fire and salvage. Tree species basal areas used for simulations represent the natural range expected in the field. Additional parameters include the abscission schedule of the species, the percentage of optimal (exposed mineral soil or humus) post-fire seedbeds, and the timing of the salvage operation. The tool also allows for rapid planning of both salvage operations and planting. It has important advantages over conventional seedling surveys in that it can be employed immediately following fire, can guide management decisions about the planning of road construction and the harvest sequence, and takes into account the entire establishment phase. Using the tool to adjust the harvesting schedule to minimize replanting, the likely final cost of reforestation can be estimated for an entire burn. The tool shows that a higher percentage of optimal seedbeds are necessary following salvage: (1) for black spruce to regenerate adequately compared to jack pine (i.e., planting of black spruce will almost always be necessary); (2) as basal area of the species of interest decreases; and, (3) to adequately regenerate burned intact and salvaged stands in late-season fires compared with those from early-season fires.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".