A binomial sampling method for estimating the density of blueberry aphid, <i>Ericaphis fimbriata</i> (Hemiptera: Aphididae), in commercial highbush blueberry
Bibliographic record
Abstract
Abstract An accurate and efficient sampling method is an important tool for insect pest management because it allows for consistent measurements across many samples. There are currently no proposed standardised sampling plans or spray thresholds for the aphid Ericaphis fimbriata Richards (Hemiptera: Aphididae) on highbush blueberry (Vaccinium corymbosum Linnaeus) in British Columbia, Canada, despite it being the primary vector for blueberry scorch virus (BlScV). A standard sampling plan for this pest would allow for rapid and consistent measurements of aphid abundance in commercial fields and would allow for more detailed study of the relationship between aphid abundance, damage, and the spread of BlScV. Binomial sampling plans use the presence:absence of a pest within a sample unit to estimate the proportion of infested sample units. Pest density (proportion of measured samples with individuals present) is linked to abundance (number of individuals), and the relationship between these two measures can be modelled mathematically. In the present study, we collected data on aphid density and aphid abundance in six varieties of highbush blueberry grown in the Fraser Valley, British Columbia. These data were used to construct a distribution-free binomial model that, when given a measure of aphid density, can predict aphid abundance within a given sample.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".