Shifting Prevalence of Plant-Parasitic Nematodes in Orchards and Vineyards of the Okanagan Valley, British Columbia
Bibliographic record
Abstract
Fruit production in the Okanagan Valley of British Columbia is dominated by apple, sweet cherry, and wine grape. The relative importance of sweet cherry and grape has increased in recent decades, but little was known of the plant-parasitic nematodes associated with those crops. Soil samples analyzed for plant-parasitic nematodes were collected from a total of 39 apple orchards, 61 cherry orchards, and 57 vineyards; most were collected in 2018, but 36 cherry orchards were sampled in 2012. Soil properties were also assessed and related to nematode population densities. Nematode genera of potential significance were, in order of prevalence, Pratylenchus, Mesocriconema, Xiphinema, Paratylenchus, Paratrichodorus, Hemicycliophora, and Meloidogyne. Pratylenchus were found in 79, 98, and 81% of the apple, cherry, and grape plantings, respectively; Mesocriconema were found in 51, 79, and 82%; and Xiphinema were found in 59, 51, and 77%. Population densities of the three dominant genera were influenced more by soil texture than any other soil characteristics, with Pratylenchus being negatively correlated with percentage clay, Mesocriconema positively correlated with percentage sand, and Xiphinema positively correlated with percentage silt. The high prevalence of Mesocriconema in cherry orchards and vineyards in this region is significant because Mesocriconema is known to be an important pest of other Prunus crop species and grapevines in other regions. This study therefore provides a rationale for increasing grower awareness and research efforts on the impacts and management of Mesocriconema and other plant-parasitic nematodes in orchards and vineyards in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".