Canopy effects on abundance and leaf traits of a spring ephemeral: <i>Erythronium americanum</i>
Bibliographic record
Abstract
Spring ephemerals take advantage of the high light levels available in the spring by completing the aboveground portion of their lifecycle before the canopy develops and while few other understory plant species are growing. The spring is marked by high resource availability, yet spring ephemerals are variably abundant throughout forests. Research indicates that canopy conditions can influence the growth of spring ephemerals; consequently, we tested whether the variation in canopy conditions predicted variation in the abundance of Erythronium americanum Ker Gawl. across 50 forest plots. We also tested whether the specific leaf area (SLA) of E. americanum in plots was predicted by variation in plot-level canopy conditions, reflecting E. americanum‘s ability to adapt to different canopy conditions. The abundance of E. americanum was significantly lower in the plots with greater hard canopy closure (i.e., permanent cover: tree architecture + evergreen leaf cover), and significantly higher under canopies that reached full development earlier. Canopies with greater hard canopy cover at the start of the growing season were associated with significantly higher SLA, quantifying local adaptation by E. americanum to variable canopy conditions. Erythronium americanum takes advantage of the high light levels available in the spring. It is unclear at this time why higher abundance of E. americanum is associated with canopies that close earlier.
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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".