Organic mulches influence population densities of root-lesion nematodes, soil health indicators, and root growth of red raspberry
Bibliographic record
Abstract
The root-lesion nematode Pratylenchus penetrans is a pathogen of red raspberry (Rubus idaeus). A field experiment was initiated in 2003 to determine the effects of organic mulches of two different composts, broiler manure, and shredded paper co-applied with broiler manure, on (i) the abundance of P. penetrans, (ii) free-living nematodes as indicators of soil food web structure, and (iii) root biomass and early productivity of ‘Malahat’ red raspberry. The organic mulches were applied each year at rates estimated to provide the 1.5 m wide root zone strip with approximately 133 kg·ha–1 of potentially available nitrogen. The nonmulched control was fertilized each year with urea at 133 kg N·ha–1. Soil samples were taken from the root zone for root biomass and nematode analyses at five dates during the 2005 and 2006 growing seasons. Population densities of P. penetrans in soil were lower and root biomass was greater under one of the composts and the shredded paper - broiler manure mulch relative to the nonmulched treatment. Fine root biomass was negatively correlated with P. penetrans per gram root. The abundance of omnivorous and predacious nematodes was negatively correlated with P. penetrans population densities and positively correlated with root biomass. The data are consistent with our hypothesis that organic mulches increase soil food web structure and the abundance of nematode antagonists (e.g., predacious nematodes), resulting in reduced crop damage by P. penetrans. However, we cannot rule out the possibility that the improved raspberry root growth and primocane productivity was the result of beneficial changes in soil chemical properties, such as increases in exchangeable calcium and pH.
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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) | 0.002 | 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".