The dynamic mosaic phenotypes of flowering plants
Bibliographic record
Abstract
Ecological interaction and adaptation both depend on phenotypic characteristics. In contrast with the common conception of the 'adult' phenotype, plant bodies develop continuously during their lives. Furthermore, the different units (metamers) that comprise plant bodies are not identical copies, but vary extensively within individuals. These characteristics foster recognition of plant phenotypes as dynamic mosaics. We elaborate this conception based largely on a wide-ranging review of developmental, ecological and evolutionary studies of plant reproduction, and identify its utility in the analysis of plant form, function and diversification. An expanded phenotypic conception is warranted because dynamic mosaic features affect plant performance and evolve. Evidence demonstrates that dynamic mosaic phenotypes enable functional ontogeny, division of labour, resource and mating efficiency. In addition, dynamic mosaic features differ between individuals and experience phenotypic selection. Investigation of the characteristics and roles of dynamic and mosaic features of plant phenotypes benefits from considering within-individual variation as a function-valued trait that can be analysed with functional data methods. Phenotypic dynamics and within-individual variation arise despite an individual's genetic uniformity, and develop largely by heterogeneous gene expression and associated hormonal control. These characteristics can be heritable, so that dynamic mosaic phenotypes can evolve and diversify by natural selection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".