Population Dynamics of <i>Mesocriconema xenoplax</i> Parasitizing Sweet Cherry Trees in British Columbia, Canada
Bibliographic record
Abstract
Abstract The ring nematode, Mesocriconema xenoplax , has become recognized as a widespread pest of sweet cherry trees in the Okanagan Valley of British Columbia (BC). Understanding the cumulative impacts of M. xenoplax on tree health, interpreting diagnostic sample data, and predicting the impacts of climate change on M. xenoplax population densities all depend on knowledge of the temporal dynamics of M. xenoplax populations and their linkage with soil temperature and moisture regimes. The objective of this study was to measure population densities of M. xenoplax on a monthly basis over five years, in relation to soil temperature and moisture regimes, in a 16-year-old irrigated sweet cherry orchard. We tested the following hypotheses: (i) population densities would start low each spring and increase continuously with soil degree-day heat accumulation during each growing season, and (ii) year-to-year variation in population growth during the growing season would be correlated with year-to-year variation in soil degree-day heat accumulation. The data did not support these hypotheses and indicated that although there were significant differences in population densities among sample dates, there were no regular seasonal cycles of population growth and decline. We suggest that in mature cherry orchards, density-dependent processes mask the influences of annual changes in soil temperature and moisture on population processes. The data indicate that for diagnostic sampling purposes, all seasons would be equally representative of M. xenoplax population densities in irrigated orchards in BC. Furthermore, the lack of any strong linkage between soil temperature regimes and within- or across-year population dynamics indicate that modeling efforts based solely on abiotic drivers of temperature and moisture would not likely represent changes in population dynamics of M. xenoplax that will actually occur with climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".