THE INTERACTION BETWEEN MASTING AND FIRE IS KEY TO WHITE SPRUCE REGENERATION
Bibliographic record
Abstract
We used the mast-seeding tree Picea glauca (white spruce) to examine wheth- er the timing of mast years relative to fire had a lasting effect on the density and timing of regeneration. We studied 17 fires that occurred in mast years and in years with low cone production between 1941 and 1994. Trees were carefully aged by crossdating procedures. Over the 59-yr period studied, there was significantly more regeneration after fires that occurred in mast years than after fires that occurred in years of low cone production. Spruce density was significantly lower after fires that occurred 1-3 years before a mast year than after fires during mast years. The cohort of trees that regenerated in the first mast year after a fire was critical to white spruce regeneration for fires that occurred 0-1 year before a mast year, but mast years occurring three or more years after a fire contributed few recruits. Our results suggest that masting is a key process that interacts with fire to shape stand composition in boreal mixedwoods. For species like white spruce, for which establishment is linked to disturbance, masting may have a contingent, historical effect on succession and landscape structure.
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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".