Mathematical Modeling of Population Dynamics of <i>Trogoderma granarium</i> (Coleoptera: Dermistidae)
Bibliographic record
Abstract
Khapra beetle, Trogoderma granarium Everts, is one of the economically important quarantine pests that mainly feeds on food grain and proteinaceous materials. Its total development time lasts approximately 40-45 d under favorable environmental conditions. Extreme temperatures, high relative humidity (RH), high larval densities, or low food quality can induce a larval diapause, where the insect can survive for up to a few years, occasionally feeding and molting. Ecological modeling is a helpful tool to study the population dynamics of biological systems. Physi-Biological age method is based on temperature-driven development rate, and factors such as RH and food quality were considered as multipliers. The objective of this study was to develop mathematical models to calculate the survival and development of adults, eggs, larvae, pupae, and oviposition and diapause under different environmental conditions such as temperature, RH, and food quality. Algorithms were developed to simulate the population dynamics for each day and coded in C++. The developed models were validated against the literature data and evaluated using linear regression, R2, and MSE. Population dynamics were simulated under Canadian grain storage conditions, and the developed models predicted that the diapausing larvae survived the extremely cold conditions found in Canadian grain. In contrast, other stages did not survive. The surviving larvae developed to pupae and adults, and females began laying eggs once the temperature became warmer in the grain bins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".