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Mathematical modeling of the interaction of populations of two biological species in an ecological system

2023· article· en· W4389939986 on OpenAlexaboutno aff
G.R. Koshchanova, Elvira Sagindykova, R.S. Shuakbaeva, D.K Semirkhanova, B.A. Mukushev

Bibliographic record

VenueBULLETIN of the L N Gumilyov Eurasian National University BIOSCIENCE Series · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsMathematical modelEcologyPredationEcosystemComputer scienceMathematicsBiologyStatistics

Abstract

fetched live from OpenAlex

The interaction of populations of two biological species in an isolated predator-prey ecosystem has been studied. Mathematical models of the interaction of these biological species are considered. The mathematical model is presented in the form of a system of differential equations. For the research, a program was created in the Mathcad environment. With the help of this program, graphical and numerical solutions of mathematical models were obtained. The dynamics of changes in the parameters of two populations has been studied. Experimental results concerning populations of two predator-prey biological species were considered and investigated. The statistical data of the hunting of the hare and lynx of the Canadian fur company are analyzed. The results of experiments on the study of argali and wolf populations in the Kokentau Nature Reserve of the East Kazakhstan region are presented. The correspondence of the theoretical results of graphical data and mathematical models of populations characterizing the interaction of animals in the argali-wolf ecosystem is proved.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0030.001

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 head0.064
GPT teacher head0.248
Teacher spread0.184 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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