Emergence of balsam fir seedlings under increasing broadleaf litter thickness
Bibliographic record
Abstract
Balsam fir (Abies balsames (L.) Mill.) seeds dispersed in autumn–winter can be covered by many layers of broadleaves when they germinate under deciduous trees during the following growing season. Our goal was to test the effect of seed density, broadleaf litter thickness, and substrate type on emergence, morphology, and dry mass allocation of balsam fir. The greenhouse experiment included three seeding densities, four litter thicknesses covering the seeds, and two substrate types. The broadleaf mixture was composed of white birch (Betula payrifera Marsh.), trembling aspen (Populus tremuloides Michx.), and pin cherry (Prunus pensylvanica L.f.) collected on the forest floor of a 30-year-old postfire stand. Seed density had no effect on emergence from under broadleaf litter. Emergence was lower on the litter substrate than on potting soil. Seedling emergence and vigor declined as broadleaf thickness increased. Depending on substrate type, emergence was reduced by 20% (leaf litter substrate) to 28% (potting soil) when seeds were covered by 1.4–2.0 cm of broadleaves. An overtopping layer of 2.5–3.0 cm reduced emergence by 57%. Balsam firs emerging from under broadleaf litter allocated more dry mass to longer and thinner hypocotyls at the expense of cotyledon and root growth, albeit seeded on potting soil. This study identifies biomechanical constraints that severely reduce early seedling vigour suggesting that in situ survival under broadleaf litter is very low.
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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 | 0.000 |
| 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".