Assessing the dominance of <i>Phleum pratense</i> cv. climax, a species commonly used for ski trail restoration
Bibliographic record
Abstract
Abstract Questions: (1) Are some species used for ski trail restoration too dominant to allow native species to re‐establish? (2) What plant traits can be used to predict which species are good competitors? We tested the hypothesis that limited native species establishment on ski trails is caused by (1) the dominance of Phleum pratense cv. climax (PPC) and (2) the asymmetry of competitive interactions. Location: Sub‐alpine area in the northern French Alps. Methods: PPC was cultivated outdoors over 2 years with 15 alpine species in a systematic design with high‐ and low‐nutrient soil conditions. For each species relative survival, competitive performance and relationships with plant traits were measured. Results: PPC exerted strong dominance on most of its neighbouring species. Survival performance of Anthyllis vulneraria, Luzula sudetica and Lotus alpinus were dramatically reduced. Results of above‐ground competition showed that species were trapped in asymmetric competition. Festuca rubra, Trifolium repens, Alchemilla xanthochlora, Trifolium pratense and Plantago alpina best counteracted PPC. Below‐ground competition was more symmetric, particularly at the high nutrient level. Plant traits such as biomass, canopy size and specific leaf area were positively correlated with competitive performance of the species. Conclusion: The study has implications for the management of restored ski trails since PPC may hinder the establishment of native sub‐alpine species. Consequently, recommendations should focus on (1) maintaining a low proportion or decreasing the proportion of PPC seeds in the revegetation mix and (2) reducing soil fertilization. Plant traits and competition experiments can help to predict changes in restored grasslands.
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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 | 0.001 | 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".