Impacts of salinity and simulated herbivory on survival and reproduction of the threatened Gulf of St. Lawrence Aster,<i>Symphyotrichum laurentianum</i>
Bibliographic record
Abstract
Halophytic plants may experience joint, and potentially interacting, effects of salinity and herbivory on their survival and reproduction. We investigated the impacts of salinity and (simulated) herbivory on fitness in the Gulf of St. Lawrence Aster ( Symphyotrichum laurentianum (Fernald) Nesom; Asteraceae). In natural populations, this species experiences varying soil salinity and spill-over herbivory from insects associated with neighbouring seaside goldenrod ( Solidago sempervirens L.). We subjected S. laurentianum individuals to three levels of simulated herbivory (0%, 20%, and 40% leaf-area removal) and three levels of salinity (0, 10, and 20 g·L–1) in three runs of a growth-chamber experiment. The effects of salinity and herbivory were always additive. Salinity consistently and strongly reduced survivorship, by as much as 80% at 20 g·L–1salt, but significantly affected the seed set of survivors in only one run. Herbivory reduced survival significantly in one run (by 42% at 40% leaf-area removal) and reduced seed set of survivors (by 36% at 40% leaf-area removal) in another. While both stresses can have important effects on S. laurentianum fitness, the effects of salinity were more consistently strong than were the effects of herbivory, for both survival and seed set.
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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) | 0.000 | 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".