Data for: Birth order as a source of within-genotype diversification in the clonal duckweed, Spirodela polyrhiza (Araceae: Lemnoideae)
Bibliographic record
Abstract
Organismal persistence attests to adaptive response to environmental variation. Diversification bet hedging, in which risk is reduced at the cost of expected fitness, is increasingly recognized as an adaptive response, yet mechanisms by which a single genotype generates diversification remain obscure. The clonal greater duckweed, Spirodela polyrhiza (L.), facultatively expresses a seed-like but vegetative form, the “turion”, that allows survival through otherwise lethal conditions. Turion reactivation phenology is a key fitness component, yet little is known about turion reactivation phenology in the field, or sources of variation. Here, using floating traps deployed in the field, we find a remarkable extent of variation in natural reactivation phenology that cannot be explained solely by spring cues, occurring over a period of at least 200 days. Under controlled laboratory conditions, we find support for the hypothesis that turion phenology is influenced jointly by phenotypic plasticity to temperature and diversification within clones. Turion “birth order” consistently accounted for a difference in reactivation time of 46 days at temperatures between 10° and 18°C, with early birth-order turions reactivating more rapidly than late birth-order turions. These results should motivate future work to formally evaluate turion phenology variance as a bet-hedging trait.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.044 |
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".