Sampling Plans for <I>Pseudaletia unipuncta</I> (Lepidoptera: Noctuidae) Larvae in Azorean Pastures
Bibliographic record
Abstract
Pseudaletia unipuncta (Haworth) is a pest in Azorean pastures that causes up to 8% yield loss mainly during summer and early autumn. The objective of this research was to develop robust sampling plans based on larval spatial distribution. The number of larvae in three 60-m2 plots on S. Miguel Island (Azores) was determined. Plots of 0.25 m2 were the most accurate unit for estimating the population size. The relationship between mean and variance fitted both Taylor’s power law and Iwao’s patchiness regression model. Relationships to determine optimum sample sizes for fixed levels of precision based on both models were developed, but demanded a heavy sampling cost for the usual precision values (0.1–0.25). To reduce sampling effort, two sequential sampling plans were developed and compared, one based on Taylor’s parameters and the other based on Iwao’s parameters. For a precision of 0.25, Taylor’s sequential sampling plan led to an average 76% reduction of the sampling effort compared with sample sizes estimated for fixed levels of precision. Simulations of Iwao’s sequential sampling plan applied to larval counts correctly predicted a “treat” or “not-treat” decision for 91% of the cases. However, this plan estimated field densities with a lower degree of precision than Taylor’s plan and required a considerable increase in sampling effort for larval densities close to the critical mean. Use of Taylor’s sequential sampling plan should provide effective management of P. unipuncta in grass pastures and minimize sampling time and cost.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".