The influence of a long-term black medic (<i>Medicago lupulina</i> cv. George) cover crop on arbuscular mycorrhizal fungal colonization and nutrient uptake in flax (<i>Linum usitatissimum</i>) under zero-tillage management
Bibliographic record
Abstract
Turmel, M. S., Entz, M. H., Tenuta, H., May, W. E. and LaFond, G. P. 2011. The influence of a long-term black medic ( Medicago lupulina cv. George) cover crop on arbuscular mycorrhizal fungal colonization and nutrient uptake in flax ( Linum usitatissimum ) under zero-tillage management. Can. J. Plant Sci. 91: 1071–1076. Leguminous cover crops are becoming a popular way to increase the sustainability of agricultural systems. Previously, cover crops have been found to increase colonization by arbuscular mycorrhizal fungi (AMF) and phosphorus and micronutrient uptake. Long-term field studies were conducted to test the hypothesis that self-regenerating black medic (Medicago lupulina cv. George) cover crops increase AMF colonization and early nutrient uptake in flax (Linum usitatissimum). Field experiments were established in 2000 (Manitoba) and 2002 (Saskatchewan) using a flax–wheat (Triticum aestivum)–oat (Avena sativa) rotation. In a second experiment, intact soil cores were harvested from the plots in spring and tested for soil disturbance and cover crop effects under controlled environment conditions (CEC). Both seedling flax crops sampled from the field in 2005 and 2006 and flax growth in CEC showed high levels of AMF root colonization, but no significant influence of the cover crop on AMF colonization by arbuscules or hyphal structures was detected. The AMF enhancing practices used in the experiments (i.e., zero-tillage and inclusion of mycorrhizal crops) may have contributed to the lack of cover crop effect on AMF colonization. The cover crop had no effect on macro- or micronutrient uptake by flax except during drought conditions (Winnipeg 2006), where flax biomass was reduced by 38% and the total uptake of N, P, Zn and Cu was decreased by 34, 30, 31 and 35%, respectively, in the medic treatment.
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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 | 0.001 |
| 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".