To move or not to move: determinants of seed retention in a tidal marsh
Bibliographic record
Abstract
1 The effects of moisture conditions, seed morphology, vegetation structure and hydrodynamic variables on seed retention were examined in a system where the dominant dispersal agent is water. Experiments were conducted in a tidal salt marsh and in a flume facility where hydrodynamic variables could be controlled. 2 Moisture condition of seeds greatly influenced which factors were most important in determining seed retention. Seed type (buoyancy) was the most important factor when seeds were dry with seeds possessing very low floating capacity (Plantago maritima) being retained in greater numbers than seeds with intermediate floating capacities (Suaeda maritima and Elytrigia atherica). 3 In contrast, hydrodynamic variables dominated retention processes when seeds were waterlogged. The application of waves in addition to flow velocity dislodged more seeds than flow velocity alone. 4 Vegetation structure influenced retention in both dry and wet conditions but less so than other factors. Denser, less rigid vegetation types retained greater numbers of seeds than more open, more rigid vegetation types. 5 Results suggest that buoyancy traits appear to determine whether seeds move in the drier summer and autumn months after initial detachment from parent plants but the intensity of wave action will determine whether waterlogged seeds stay in a microsite during the wetter months of late autumn to early spring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".