Binomial Sampling of Western Flower Thrips Infesting Flowering Greenhouse Crops Using Incidence-Mean Models
Bibliographic record
Abstract
Accurate assessments of thrips density are important for effective thrips management programs. Complicating the development of sampling plans for western flower thrips (WFT) Frankliniella occidentalis (Pergande) in greenhouse crops are the facts that they are small, difficult to detect, and attack a variety of crops, which may be grown concurrently within the same greenhouse. Binomial sampling was evaluated as an alternative to sampling plans based on complete enumeration. This work included comparison of incidence-mean models across diverse plant species (impatiens, cucumber, and marigold) to determine the possibility of using a generic model for sampling WFT in mixed crops. Data from laboratory-processed flower samples revealed that infestation rates calculated using a tally threshold of three thrips per flower provided the best estimates of thrips population densities in each tested crop and in the combined crops (composite data set). Distributions of thrips populations were similar across the three plant species, indicating potential for development of a generic sampling plan for mixed floral crops. Practical sampling methods for simple and complex flowers tested in the greenhouse (in situ) were evaluated via construction of binomial count operating characteristic functions. In the case of simple flowers (impatiens), visual inspections provided adequate estimates of thrips infestation rates at a low tally threshold, which ultimately enabled accurate estimation of thrips densities. However, visual inspection and tap-sampling of complex flowers (marigold) provided unreliable results. These findings indicate that use of binomial sampling methods in mixed floral crops will require development of more accurate sampling techniques.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".