Cotyledon damage affects seed number through final plant size in the annual grassland species Medicago lupulina
Bibliographic record
Abstract
BACKGROUND AND AIMS: The effects of cotyledon damage on seedling growth and survival are relatively well established, but little is known about the effects on aspects of plant fitness such as seed number and size. Here the direct and indirect mechanisms linking cotyledon damage and plant fitness in the annual species Medicago lupulina are examined. METHODS: Growth and reproductive traits, including mature plant size, time to first flowering, flower number, seed number and individual seed mass were monitored in M. lupulina plants when zero, one or two cotyledons were removed at 7 d old. Structural equation modelling (SEM) was used to examine the mechanisms linking cotyledon damage to seed number and seed mass. KEY RESULTS: Cotyledon damage reduced seed number but not individual seed mass. The primary mechanism was a reduction in plant biomass with cotyledon damage that in turn reduced seed number primarily through a reduction in flower numbers. Although cotyledon damage delayed flower initiation, it had little effect on seed number. Individual seed mass was not affected by cotyledon removal, but there was a trade-off between seed number and seed mass. CONCLUSIONS: It is shown how a network of indirect mechanisms link damage to cotyledons and fitness in M. lupulina. Cotyledon damage had strong direct effects on both plant size and flowering phenology, but an analysis of the causal relationships among plant traits and fitness components showed that a reduction in plant size associated with cotyledon damage was an important mechanism influencing fitness.
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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".