Costs of reproduction in flowering plants
Bibliographic record
Abstract
Costs of reproduction arise when investments into current reproduction reduce future reproductive fitness. Studies on reproductive costs use diverse approaches, including the analysis of gene expression, physiology, trade-offs between reproduction and growth/survival, and the impact of reproductive investments on population growth. These studies demonstrate that reproductive trade-offs have far-reaching effects on plants, affect their fitness, and are therefore important for shaping the evolution of life histories. However, not all studies have detected costs of reproduction, and c. 90% of these were conducted in natural populations, where controlling for variation in plant resource status is challenging. For dioecious plants, there is a common perception that fruit production should result in greater costs of reproduction for females than males, but divergent reproductive costs between the sexes are not supported by studies of reproductive trade-offs in dioecious plants. Other aspects of reproductive costs remain poorly understood, including ecological costs of reproduction, the fitness effects of reproductive trade-offs involving growth or physiological processes, and how the male sex role influences reproductive costs. Progress will be enabled by the use of measurements that allow for easier comparisons across studies and by more clearly distinguishing between the processes that contribute to current vs future reproductive fitness.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".