Demography of Rusty Grain Beetle in Stored Bulk Wheat: Part II. Mathematical Modeling to Characterize and Predict Population Dynamics
Bibliographic record
Abstract
Data collected in Part I of this study were further analyzed by using mathematical modeling methods. Out of the nine unstructured population models tested, no model could fit the insect numbers under all of the tested conditions. This analysis showed that Cryptolestes ferrugineus (Stephens) (Coleoptera: Laemophloeidae) inside small patches (50 ml volume) had different characterization of population dynamics from that inside large patches (18 liter volume) and had different population demography when the insect number at the previous time was different. The key factor analysis showed that the first two main factors influencing the population dynamics were the temperature and the previous insect numbers. The total numbers of insects increased with the increase of sum of degree days. However, the degree day model developed based on the constant temperatures could not predict insect numbers under fluctuating temperatures. A newly developed model, which used the result of the unstructured population models, key factor analysis, and the degree day model, could explain about 66% of the insect numbers under fluctuating temperature conditions.
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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) | 0.000 | 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) | 0.000 | 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".