Using among-year variation to assess maternal effects in<i>Pinus aristata</i>and<i>Pinus flexilis</i>
Bibliographic record
Abstract
Maternal effects, the effect of the maternal environment during development on offspring growth, can complicate the interpretation of common garden studies. Growing one or more generations in a common environment can help minimize maternal effects, but is often not practical with long-lived species. In Pinus aristata Engelm. and Pinus flexilis James, we assessed maternal effects by growing offspring sourced over multiple years from the same mother trees, comparing growth traits between source years. Additionally, we explored the effect of maternal environment on seed characteristics by collecting five twig clippings from each mother tree and measuring characteristics indicative of the relative vigor of the tree during each seed source year. The effect of year was significant for twig growth characteristics, seed size, and seedling performance. For both species, there were significant relationships between the relative inter-annual (RIA) variation in seed mass and the RIA variation in numerous seedling traits including cotyledon length, seedling total dry mass, and needle length. Variation in seed mass was not predicted by yearly variation in the maternal plant’s phenotypic traits. These results support the hypothesis that maternal effects translate into variation in early seedling growth and suggest possibilities to statistically account for them in common garden studies involving long-lived species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".