A test of three juvenile plant competitive response strategies
Bibliographic record
Abstract
Abstract Questions: 1. Are there competitive response strategies for light in juvenile plants? 2. If so, do plant traits (e.g. seed weight, relative growth rate, height and biomass) correlate with the strategies? Location: Controlled greenhouse study using perennial vegetation typical of wet meadows in Northeast Ohio, USA. Methods: We used two light manipulations in a greenhouse to screen ten replicates of 19 plant species for three proposed competitive response strategies (‘escape’, ‘forage’, ‘persist’). We measured the time it took a seedling to die and the maximum height achieved when grown in the dark to assess two strategies, persist and escape. The biomass of seedlings when grown under a controlled, low‐intensity, shifting light source was measured to test a third strategy, forage. Results: We found significant variation across species in the measurements used to assess each strategy. The species ranking for each strategy was not concordant across strategies. Traits were found that correlated with the escape strategy (seed weight, height and biomass) and persist strategy (time to reach maximum height). No traits were found that correlate with the forage strategy. Conclusions: There appear to be trade‐offs by plants in the three strategies tested in this study. Species which had the best performance on one strategy typically scored poorly on the other strategies. However, many species fall in the middle range, ranking similarly across the ‘persist’, ‘escape’, and ‘forage’ strategies.
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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 | 0.000 |
| 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".