Above- and below-ground interactions between <i>Lotus tenuis</i> and <i>Cynodon dactylon</i> under different fertilization levels
Bibliographic record
Abstract
Llobet, M., Vignolio, O. R., Savé, R. and Biel, C. 2012. Above- and below-ground interactions between Lotus tenuis and Cynodon dactylon under different fertilization levels. Can. J. Plant Sci. 92: 45–53. Mixtures of legumes and grasses are recommend for xeric garden fields, landscape restorations and to improve productivity, digestibility and quality of grasslands and pastures, due to the complementarity in the use of resources, their resistance to wide edaphoclimatic conditions and the nitrogen-fixing ability of the legume. However, legumes persistence is poor. Two greenhouse studies examined the relative importance of above- and below-ground competition and root physiological attributes between a legume Lotus tenuis and a grass bermudagrass (Cynodon dactylon) under two fertilization levels. The plants were arranged using the ″Divided pot" technique during 34 wk. Root competition was more important on the performance of both species than shoot competition. Cynodon dactylon had greater nutrient use efficiency than L. tenuis. The low root hydraulic resistance and high specific root length of C. dactylon could enhance its ability to explore the soil and compete for water and nutrients more efficiently than L. tenuis, which compensates for these characteristics with a higher root:shoot ratio than C. dactylon. Although mechanisms involved in root competition between species are not fully understood, our results show that root physiological and morphologic traits contribute to understanding the responses of the species in mixture.
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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".