An Artificial Neural Network Model to Predict Wheat Stem Sawfly Cutting in Solid-Stemmed Wheat Cultivars
Bibliographic record
Abstract
The wheat stem sawfly, Cephus cinctus Norton (Hymenoptera: Cephidae), is a major pest of wheat (Triticum aestivum L.) in the northern Great Plains of North America. The use of solid-stemmed cultivars helps mitigate crop losses and can also affect the survivorship of C. cinctus. The efficacy of a plant’s resistance is based on its ability to develop pith in the culm of the stem, which is influenced greatly by interactions between the genotype and environment. Precipitation-related weather interacts with photoperiod to reduce pith expression in solid-stemmed wheat. A model that predicts pith expression could serve as a management tool to prevent losses by alerting producers if in-season precipitation patterns have caused less than ideal pith expression in a cultivar. Artificial Neural Network (ANN) models are used to make predictions for complex, non-linear systems with many co-related variables. Our objective was to improve upon past models that used regression analyses by deploying an ANN model to predict in-season stem cutting of wheat by wheat stem sawfly. Results indicate that stem cutting is influenced by the precipitation within a 5 wk period from 1 June to 5 July. These results were successfully deployed in a model that should assist with predictions of potential late season stem cutting. Deployment of this ANN model as a transferable executable file may facilitate predictions of stem cutting by wheat stem sawfly in any given year, which will empower producers to implement the appropriate harvest management strategies to reduce losses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".