Postfire seed rain of black spruce, a semiserotinous conifer, in forests of interior Alaska
Bibliographic record
Abstract
The availability of viable seed can act as an important constraint on plant regeneration following disturbance. This study presents data on seed quantity and quality for black spruce ( Picea mariana (Mill.) B.S.P.), a semiserotinous conifer that dominates large areas of North American boreal forest. We sampled seed rain and viability for 2 years after fire (2005–2007) in 39 sites across interior Alaska that burned in 2004. All sites were dominated by black spruce before they burned. Structural equation modeling was used to assess the relative importance of prefire spruce abundance, topography effects, canopy fire severity, and distance to unburned stands in explaining variations in black spruce seed rain. Prefire basal area of spruce that remained standing after fire was a significant predictor of total seed rain, but seed viability was more strongly related to site elevation, canopy fire severity, and distances to unburned stands. Although positive relations between tree basal area and the size of the aerial seed bank may place a first constraint on seed availability, accurate prediction of postfire viable seed rain for serotinous conifers also requires consideration of the effects of abiotic stress and canopy fire severity on seed viability.
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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) | 0.000 | 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 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".