Sequential decision plan for controlling <i>Mamestra configurata</i> in spring canola
Bibliographic record
Abstract
Abstract A sequential decision plan was developed for controlling larvae of the bertha armyworm, Mamestra configurata Walker (Lepidoptera: Noctuidae), in canola (Brassica napus L. and B. rapa L., Brassicaceae), using 0.25 and 0.5 m2 sampling units. Fields in Manitoba were sampled from 1980 to 1994 to determine minimum sample sizes and upper and lower cumulative larval counts at three economic thresholds. Taylor’s power law described most of the variation between mean larval density and variance for 0.25 m2 (r2 = 0.926) and 0.5 m2 (r2 = 0.924) samples. Larvae were found to have a moderately clumped distribution in canola (b = 1.42). Levels of precision (D0) varying from 0.15 to 0.25 caused minimum sample sizes to vary between 6 and 21 for the 0.5 m2 samples to between 9 and 31 for the 0.25 m2 samples, for an economic threshold of 16–24 larvae/m2 (P = 0.20). Mean sampling times ranged from 40–108 for the 0.25 m2 samples to 49–126 min for the 0.5 m2 samples. The sampling plan for the 0.25 m2 samples was verified in 18 fields in 2006 and 2007. A correct decision was made in 87% (D0 = 0.25), 91% (D0 = 0.20), and 94% (D0 = 0.15) of the fields when the recommendation was to spray if a decision could not be reached after a second sampling. The mean number of samples needed for making a decision was 14 (D0 = 0.25), 19 (D0 = 0.20), and 32 (D0 = 0.15). We recommend that growers use a precision level of 0.20 to minimize error rates and sampling effort. In most years, the minimum number of 0.25 m2 samples per field that growers would need to take is 14–17.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".