Effect of seed mass on early seedling growth of five eastern white pine (<i>Pinus strobus</i>L.) families under contrasting light environments
Bibliographic record
Abstract
The strong influence of seed mass on early seedling growth of forest tree species is well established, but the ecological role of intraspecific variation in seed mass on natural regeneration is poorly understood. Wide variation in initial spatial patterns of natural regeneration of eastern white pine (Pinus strobus L.) between managed and undisturbed white pine forests of the Great Lakes region of North America has been attributed to differences in understory light levels and the frequency of substrates suitable for germination and seedling establishment. To explore the potential influence of seed mass on these observed patterns, the interaction of seed mass and light on early growth of white pine was investigated in a greenhouse study. Seedlings of five half-sib families differing in mean seed mass were grown for 8 and 14 weeks under moderate and low light conditions representing managed and undisturbed pine stands, respectively. Family differences in seedling biomass and root system development under each light environment were associated with positive, linear relationships with mean seed mass. Family and seed mass had a comparatively weak influence on biomass partitioning and relative growth rate. Moderate light improved seedling growth regardless of seed mass, but relative expression of seed mass effects on seedling traits was unaffected by light environment. These results suggest differential recruitment of white pine in managed and undisturbed stands is unrelated to variation in seed mass.Key words: biomass allocation, eastern white pine, greenhouse experiment, natural regeneration, relative growth rate, seed mass.
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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) | 0.000 | 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) | 0.001 | 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".