Are mineral soils exposed by severe wildfire better seedbeds for conifer regeneration?
Bibliographic record
Abstract
We examined jack pine (Pinus banksiana Lamb.), black spruce (Picea mariana (Mill.) BSP), and white spruce (Picea glauca (Moench) Voss) seed germination and seedling recruitment in aspen (Populus tremuloides Michx.) and conifer mixedwood stands following the 1999 Black River fire in southeastern Manitoba, Canada. Three postfire seedbed types were tested: scorched (surface litter only partially consumed), lightly burned (surface litter consumed with little or no duff consumption), and severely burned (complete consumption of litter and duff exposing mineral soil). Seeds were sown in 1999, 2000, and 2001, and each cohort was monitored for 3 years. In 1999, severely burned seedbeds had poor germination, while scorched seedbeds had the highest germination. The reverse was true in 2001. After the first growing season, continued survival of seedlings was greater on severely burned seedbeds for all three cohorts. However, better survival on severely burned seedbeds was not sufficient to overcome poor germination in 1999 and 2000. When using artificial seeding to promote conifer regeneration, we recommend a delay of one full year after a severe spring fire for jack pine and two full years for black spruce and white spruce on boreal aspen and conifer mixedwood sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".