Modeling seed dispersal of black cherry, an invasive forest tree: how microsatellites may help?
Bibliographic record
Abstract
We used empirical models and three dispersal functions (Weibull, lognormal and 2Dt) to model seed distributions derived from the black cherry (Prunus serotina Ehrh.) understorey of a pine-dominated stand. Two different approaches were used to disentangle the overlapping seed shadows: the traditional inverse modeling approach and the genetic approach that uses microsatellite markers to assign a dispersed seed to its maternal parent. The distinction was made between the seeds passively dispersed by gravity (fruits with mesocarp) and those dispersed by birds (fruits without mesocarp). Our main objectives were to compare the three dispersal functions and assess the differences between the two approaches used. The functions performed equally well, but the lognormal function often showed a better data correlation. The best dispersal curves obtained by both traditional and genetic approaches were quite similar for the seeds dispersed by gravity, and 95% of these seeds were predicted to fall 5 and 3 m away from the parent tree for the traditional and genetic approaches, respectively. Differences were more important for the seeds dispersed by birds. The traditional approach predicted a lower number of seeds near the parent plant and a higher dispersal distance. Microsatellites provided accurate information on individual dispersal events and led to a better insight into the dispersal process.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".