A model for the overwintering process of European grapevine moth Lobesia botrana (Denis & Schiffermuller) (Lepidoptera, Tortricidae) populations
Bibliographic record
Abstract
The paper deals with the development, parametrization and validation of a phenology model of the overwintering process of European grapevine moth Lobesia botrana (Denis & Schiffermüller) populations in northern latitudes. The model is built on diapause and poikilothermic population development theories and represents the phenological events of entries into and emergence from pre-diapause, diapause and post-diapause phases. The rate sum models for pre-diapause and post-diapause development are based on published non-linear temperature dependent rate functions. The rate sum model for diapause, however, is negatively affected by the photoperiod during diapause and positively influenced by the photoperiod at the time of diapause entry. The diapause model is parametrized with 3-year data from 25 locations in Europe and Cyprus, and validated with 1-3 year observations from 18 locations in Europe and California. Despite restrictive assumptions and limitations imposed by weather data recorded at variable distances from the observation sites, and the variable qualities of observation data, the model’s predictive and explanatory capabilities are useful for adaptive pest management and assessments of the invasive potential. The need for controlled experiments is recognized and suggestions are made for improving the model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".