Can competitive ability predict structure in experimental plant communities?
Bibliographic record
Abstract
Abstract Question: Two questions were posed: 1. Can an independent measure of relative competitive ability be used to predict the abundance of species in mixtures? 2. Is the success of those predictions affected by low fertility (stress simulation) or clipping (disturbance simulation)? Location: Greenhouse at Carleton University, Ottawa, Canada. Methods: We collected adult plant ramets of 11 species from the field and transplanted one ramet of each species into 56 containers of 60 L. We applied a 4 × 2 factorial combination of fertilization (none, full nutrients except N, full nutrients except P, full nutrients) and clipping (no clipping, clipping to 10 cm above soil) with seven replicates of each treatment. After two growing seasons the above‐ and below‐ground biomass of each species was determined. Results: Regression analyses uncovered a significant positive relationship between plant biomass (measured in this study) and relative competitive ability (as measured in an independent study) under all experimental conditions. Both the mean slope and meanR2were lowest in treatments with low nutrients and highest in the full nutrient treatment (irrespective of clipping). Conclusions: Our results show that (1) at high fertility, relative competitive ability can generally predict the abundance of species in experimental plant communities, and (2) the intensity of competition (inferred from the magnitude of the slope orR2) increased with increasing nutrient supply, particularly nitrogen.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".