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Record W6983334656

Mathematical modelling of population dynamics of khapra beetle (Trogoderma granarium)

2021· dissertation· en· W6983334656 on OpenAlexafffundabout

Bibliographic record

VenueMspace (University of Manitoba) · 2021
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsUniversity of Manitoba
FundersAgriculture and Agri-Food Canada
KeywordsTrogoderma granariumDiapausePopulationLarvaPEST analysisAge structure
DOInot available

Abstract

fetched live from OpenAlex

Trogoderma granarium Everts (Coleoptera: Dermestidae), generally known as the khapra beetle, is found in the hot and dry regions of North Africa, the Middle East, and India. It is not established elsewhere and is classified as a quarantine pest in Canada and several other countries. It is one of the economically important quarantine pests that mainly feeds on food grain and proteinaceous materials. Its total lifespan lasts approximately 40 to 45 d under favourable environmental conditions. Extreme temperatures, high 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. The diapausing larvae are resistant to heat, cold, and insecticides, that aids in surviving the adverse conditions. Ecological modelling is a helpful tool to study the population dynamics of biological systems. The objective of this study was to develop mathematical models to calculate the survival and development of the khapra beetle under different environmental conditions such as temperature, RH, and food quality. The developed models were used to estimate the development and survival of the khapra beetle under Canadian grain storage conditions. The factors affecting the development and mortality of the khapra beetle were reviewed, and appropriate assumptions were made for developing the mathematical equations using the Physi-Biological age method. This method is based on temperature-driven development rate, and factors such as RH and food quality were considered as multipliers. Mathematical equations were developed to calculate the development and mortality of adults, eggs, larvae, pupae, and oviposition and diapause under different environmental conditions. 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 it was found that the diapausing larvae survived the extremely cold conditions found in Canadian grain. In contrast, insects in 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.190
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes3
Has abstractyes

Explore more

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