The influence of biofertilizer effect on switchgrass (Panicum virgatum) crop yield under greenhouse and field conditions in Guelph, Ontario, Canada
Bibliographic record
Abstract
Greenhouse and field experiments were conducted to test the effectiveness of biofertilizers on switchgrass (Panicum virgatum) yields compared to inorganic fertilizer and a zero-control. In the greenhouse, Variovorax paradoxus JM63, JumpStart® (Penicillium bilaii), inorganic fertilizer and control treatments resulted in significantly higher per L pot biomass yields compared to the control treatment; 2.74 (±0.24), 2.55 (±0.10), 2.52 (±0.24) and 1.34 (±0.09) g L-1, respectively. As JumpStart® is a commercially available biofertilizer, it was used in the field experiment along with inorganic and control treatments. All three treatments were applied to established (2014) switchgrass plots. Significantly (p<0.05) higher biomass yields of 10.73 (±1.33) and 7.67 (±0.30) Mg ha-1 were recorded for JumpStart® and inorganic fertilizer treatments, respectively, when compared to the zero-control biomass yield of 5.36 (±0.87) Mg ha-1. The enumeration soil test revealed that soil from JumpStart® and zero-control treatments had 20033, and <100 cfu/g of soil of Penicillium spp., respectively. Results suggest that commercially available JumpStart® could replace/supplement inorganic fertilizer application on well-established switchgrass fields but, its influence on long-term biomass yields need to be further verified.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".