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Record W2910533578 · doi:10.18280/mmep.050420

Mathematical analysis of a model on guava for biological pest control

2018· article· en· W2910533578 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPEST analysisPest controlBiological pest controlMathematical modelBiologyAgroforestryBiological systemMathematicsAgricultural engineeringAgronomyEcologyBotanyEngineeringStatistics

Abstract

fetched live from OpenAlex

A model of non-linear ordinary differential equations has been formulated for the interaction between guava pests and natural enemies. This model is based on Lotka-Volterra model. We have found the equilibrium points and checked the stability of that equilibrium points, positivity analysis. Using a natural treatment, we have modified the model and also analysed the stability of the equilibrium points. Our study is established on natural treatment which is the demand of the present world. Because, now a days, there are excessive uses of chemicals in foods which cause too much harm to our health and environment. It is known that natural treatments and parasitoids (natural enemies) play a vital role in limiting the pest population. So, we can use natural controls (natural treatment and natural enemies) instead of chemicals which are healthful for crops, animals and environment. So, our aim is to minimize the pest on guava using natural treatment and natural enemies.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.285

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 head0.053
GPT teacher head0.240
Teacher spread0.188 · 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