Prediction of Gypsy moth (Lymantria dispar) outbreaks under climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Achieving the strategical goals in forestry of Republic of Serbia will not be easy in the light of climate change. Gypsy moth (Lymantria dispar L.) is the most economically significant and abound at pest in deciduous forests in Serbia. It is also very important pest in fruit orchards. His outbreak often has the character of a natural disaster that requires a significant commitment of manpower and financial resources in order to suppress it. We developed two models for predicting the occurrence of gradation (outbreak) and latency of gypsy moth population on the basis of monthly and quarterly values of climatic data for the period 1888-2010. The models were based on logistic regression. In the MODEL I, we have used the mean monthly temperatures from October of the year preceding event, temperature in January and March, and the rainfall in May, while in MODEL II, taken were mean temperatures of the first quarter and sum of the precipitation of the second quarter. Overall classification accuracy of the models were above 70%, while the prediction of outbreak based on MODEL I was 86%. The results of this study (models that can be applied in real time) can contribute to better decision-making in relation to forest management and protection of forests from gypsy moth in Republic of Serbia and wider.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it