Seedling recruitment in subalpine grassland forbs: Predicting field regeneration behaviour from lab germination responses
Bibliographic record
Abstract
Environmental cueing that restricts seed germination to times and places where mortality risk is relatively low may have considerable selective advantage. The predictive power of lab germination responses for field regeneration behaviour is rarely tested. We screened 11 alpine grassland forbs for germination behaviour predictive of microsite and seasonal selectivity, and seed carry-over across years. The predictions were tested in a field experiment. Germination in the lab ranged from 0.05% to 67.9%, and was affected by light (5 species), temperature (6 species), fluctuating temperatures (4 species), moist chilling prior to germination (cold-stratification) (6 species), and dormancy-breaking by means of gibberellic acid (8 species). Seedling emergence in the field varied from 0.1% to 14.1%, and increased in low-competition microsites (bare-ground gaps and cut vegetation; 7 species), and showed seasonal timing (1 species in autumn and 1 species in spring), and seed carry-over across years (7 species). Lab germination responses successfully predicted microsite selectivity in the field and to some extent seed carry-over across years but not seasonal timing of germination. Gap-detecting species were generally small-seeded, low-growing, and found in unproductive habitats. Larger-seeded species germinated in all of the microsites but experienced increased mortality in high-competition microsites. Seed carry-over across years was lower for alpine specialists than for more widely-distributed species.
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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 | 0.001 |
| 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".