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Record W2004447716 · doi:10.1142/s0218127414501144

Discrete Switching Host-Parasitoid Models with Integrated Pest Control

2014· article· en· W2004447716 on OpenAlexaff
Changcheng Xiang, Zhongyi Xiang, Sanyi Tang

Bibliographic record

VenueInternational Journal of Bifurcation and Chaos · 2014
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsHost (biology)ParasitoidAttractorBifurcationMathematicsControl theory (sociology)Stability (learning theory)PopulationEconomic thresholdStatistical physicsComputer sciencePEST analysisBiological systemEcologyControl (management)BiologyMathematical analysisPhysicsNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

The switched discrete host-parasitoid model concerning integrated pest management (IPM) has been proposed in the present work, and the economic threshold (ET) is chosen to guide the switches. That is, if the density of host (pest) population increases and exceeds the ET, then the biological and chemical tactics are applied together. Those multiple control measures are suspended once the density of host falls below the ET. Firstly, the existence and stability of several types of equilibria of switched system have been discussed briefly, and two- or three-parameter bifurcation diagrams reveal the regions of different types of equilibria including regular and virtual equilibria. Secondly, numerical bifurcation analyses show that the switched discrete system may have very complex dynamics including the co-existence of multiple attractors and switched-like behavior among attractors. Finally, we address how the key parameters and initial values of both host and parasitoid populations affect the host outbreaks, switching frequencies or mean switching frequency, and consequently the relative biological implications with respect to pest control are discussed.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.279
Teacher spread0.267 · 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 designTheoretical or conceptual
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

Citations19
Published2014
Admission routes1
Has abstractyes

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