A demographic assessment of the Lansing Effect in duckweed ( <i>Lemna turionifera</i> Landolt)
Bibliographic record
Abstract
The Lansing Effect is the propensity for offspring of older parents to have shorter lifespans than offspring of younger parents. A recent review identified two demographic patterns that can produce the Lansing Effect: (i) a greater offspring mortality rate at all offspring ages in offspring of older versus younger parents (greater initial mortality parameter); and (ii) an offspring mortality rate that increases more rapidly with offspring age in offspring of older versus younger parents (greater mortality rate parameter). Here, we report on a longitudinal study designed to investigate these patterns, using the duckweed Lemna turionifera. We tracked asexually produced offspring that detached from parents that were comparatively young versus old (first versus fifth offspring, respectively). Offspring of older parents lived 15% shorter, on average, than offspring of younger parents, providing evidence of the Lansing Effect. A model-selection approach revealed that the difference between survival curves of first versus fifth offspring was mainly attributable to greater initial mortality in fifth compared to first offspring, though alternative models also received some support. Our study provides a demographic explanation for the Lansing Effect in L. turionifera in particular and provides a method for assessing the survival patterns underpinning the Lansing Effect in general.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".