Phenotypic plasticity of life-history characters in response to different germination timing in two annual weeds
Bibliographic record
Abstract
An experimental manipulation was conducted to test whether germination timing influences the post-germination life-history characters in Amaranthus retroflexus L. and Chenopodium glaucum L. Seeds were sown in spring, late spring, and summer. Life-history characters of both phenology and morphology were measured, and dry masses of roots, stems, leaves, and reproductive organs were determined. Life-history characters showed high plasticity in response to different sowing dates. Later germinating plants had relatively faster growth rates and smaller sizes at reproduction than earlier germinating plants. Delaying germination led to relatively earlier reproduction and a relatively greater allocation to reproduction. Much of the variation (60%) could be explained by a single axis of a principal component analysis. The attributes on this axis were similar to the CR axis of Grime's CSR model. Further, the sowing dates of these two species were aggregated on this axis such that spring germinators tended towards the competitor strategy (C), late-spring germinators tended towards a mixed competitiveruderal strategy (CR), and summer germinators tended towards a ruderal strategy (R). Different germination timing led to different life-history strategies in the established phase. This kind of phenotypic plasticity in life history results from the plant adapting to regeneration strategies of different germination timing.Key words: Amaranthus retroflexus, Chenopodium glaucum, phenotypic plasticity, life-history characters, plant strategies, germination timing.
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 | 0.000 |
| 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".