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Record W2966033992 · doi:10.1088/1361-6544/ab231c

Speed of the traveling wave for the bistable Lotka–Volterra competition model

2019· article· en· W2966033992 on OpenAlexafffund
Manjun Ma, Zhe Huang, Chunhua Ou

Bibliographic record

VenueNonlinearity · 2019
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsMemorial University of Newfoundland
FundersZhejiang Sci-Tech UniversityNatural Science Foundation of Zhejiang ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBistabilityMultivibratorTraveling waveMathematicsNonlinear systemWave speedCompetition modelCompetition (biology)Range (aeronautics)Control theory (sociology)Mathematical analysisPhysicsComputer scienceLawEngineeringOptoelectronicsVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This paper is devoted to investigating the propagation speed and direction of the bistable traveling wave for the Lotka–Volterra competition model with bistable nonlinearity. A range-estimation of the bistable wave speed is provided by comparing the bistable traveling wave with the monostable ones in the system. Comparison theorems on the speed and direction of wave propagation are first established by the method of upper and lower solutions. Through the construction of novel upper and lower solutions, we obtain some new criteria that explicitly determine the speed sign of the bistable traveling wave. Based on these results, we can predict which species will win in the competition for the same resources.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.308
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations55
Published2019
Admission routes2
Has abstractyes

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