Clonal development of <i>Maianthemum dilatatum</i> in forests of differing age and structure
Bibliographic record
Abstract
The development of a dense tree layer in young coniferous stands can suppress understory plants, leading to very low herb abundance and diversity. In this study, clonal development of the rhizomatous herb Maianthemum dilatatum (Wood) Nels & Macbr. was compared among four types of coniferous forest (young, closed canopy; young, silviculturally thinned; mature; and old growth) on the western Olympic Peninsula, Washington. We predicted that (i) ramet turnover would be lowest, (ii) clonal fragment size would be smallest, and (iii) allocation of resources to leaves would be greatest in young, closed-canopy forests, and that these traits would increase (or decrease for leaves) as understory conditions became more favorable with stand development or thinning. The low frequency of new ramets in young, closed-canopy stands supported the first prediction. The second prediction was also supported: lateral spread and rhizome mass were smallest in these stands. However, allocation to leaves was not higher in dense young stands, indicating that Maianthemum does not respond to stress by increased investment in leaves. Clonal fragments in thinned, mature, and old stands showed no differences in traits, suggesting that once tree canopies rise, canopy gaps form, or young stands are thinned, resource levels are favorable for clonal growth. Maianthemum appears to persist in dense, young stands by maintaining long-lived ramets that produce leaves annually, rather than by increasing rhizome spread, rhizome storage, or allocation to leaves.Key words: age structure, biomass allocation, canopy closure, forest herb, rhizome.
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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".