Organic mulches and irrigation affect <i>Mesocriconema xenoplax</i> and <i>Pratylenchus penetrans</i> under cherry
Bibliographic record
Abstract
Abstract Mesocriconema xenoplax and Pratylenchus penetrans are important plant parasitic nematodes of cherry trees, but little is known of how soil and water management practices affect the buildup of either species in cherry orchards. A split-plot field experiment was initiated in 2014 to compare five soil treatments (untreated control, preplant fumigated, compost, bark chip mulch, compost+bark chip mulch) under drip and microsprinkler irrigation. Plant-parasitic nematode populations were monitored through 2023. The population of M. xenoplax was initially detected in only 3% of the 60 plots whereas P. penetrans was initially present in all plots. By 2023, M. xenoplax were detected in 70% of plots with maximum population density among plots of 834 M. xenoplax 100 cm −1 soil. Mesocriconema xenoplax became more abundant in compost plots and fumigated plots than in untreated plots, and more abundant under drip than microsprinkler irrigation. In contrast, P. penetrans were least abundant in compost plots and less abundant under drip than microsprinkler irrigation. The opposing responses of these two nematode species illustrate tradeoffs in pest pressures that can occur with changes in orchard soil and water management practices, obscuring effects of either species on tree growth.
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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".