Flowering and seedling production of understory herbs in old-growth forests affected by 1980 tephra from Mount St. Helens
Bibliographic record
Abstract
To determine the variation in flowering among species and the contribution of flowering to the recovery of forest herbs, we counted the flowering shoots, vegetative shoots, and first-year seedlings in old-growth forests affected by tephra from the 1980 eruption of Mount St. Helens. Using permanent 1 m2 plots with either undisturbed tephra or with the tephra removed, we obtained 2–9 years of flowering data during 1980–2000 for six sites that received 2–15 cm of tephra. Flowering was infrequent for most species. Most of the 12 commonest species had 2%–4% of their shoots flowering (range 0%–15%). Among growth forms, deciduous nonclonal species flowered most. Flowering percentage increased with plant density; thus microsites favorable for growth were also favorable for flowering. Flowering varied considerably among sampling years. Incidence of flowering was higher on shallow than on deep tephra, and higher on natural tephra than in cleared plots. Seedlings were more common on shallow than on deep tephra. The 20-year increase in shoot density on tephra (ratio of 2000 to 1981 values) was positively related among species to the number of seedlings produced per previous-year flowering shoot. Our results indicate how species vary in the quantity and timing of flowering and also in their reliance on seedling establishment versus clonal growth for population increase following disturbance.
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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 | 0.001 | 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".