Seedling emergence and survival of <i>Warea carteri</i> (Brassicaceae), an endangered annual herb of the Florida Scrub
Bibliographic record
Abstract
Seedling emergence and survival to flowering are critical processes in the life history of annual plants. We evaluated the importance of year and habitat on seedling emergence and the effects of year, habitat, timing of seedling emergence, and seedling density on survival of Warea carteri Small, an annual mustard endemic to Florida scrub. We tagged 1329 seedlings in 78 permanent 0.25 m2 quadrats in two habitats (scrub and disturbed) between 1999 and 2002 and followed seedlings monthly. Most (>80%) seedling emergence occurred between September and December. Emergence peaked 2 months earlier and was more variable in disturbed sites than in scrub. Seedling survival among years ranged from 3.5% to 12.0%. Seedling density varied from 1 to 75 per 0.25 m2 quadrat, with an overall median of 6.0. Survival was not density dependent. Median age at flowering ranged from 11.7 to 15.2 months, with late season recruits most likely to survive to flowering. Warea carteri recruits like a winter annual but flowers like a summer annual. Its delayed germination and a 12–15 month life span contribute to population cycling. Complete reproductive failure in unfavorable years, high seed production in favorable years, low rates of seed germination, and a persistent seed bank are consistent with predictions for an annual species in a variable habitat.
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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".