MétaCan
Menu
Back to cohort
Record W1501488288 · doi:10.5109/12866

Development of an Intelligent Robot for an Agricultural Production Ecosystem (II) : Modeling of the Competition between Rice Plants and Weeds

2008· article· en· W1501488288 on OpenAlexaff
Alejandro Isabel Luna-Maldonado, Yusuke Yamaguchi, Midori Tuda, Kei Nakaji

Bibliographic record

VenueJournal of the Faculty of Agriculture Kyushu University · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsNutrasource
Fundersnot available
KeywordsCompetition (biology)AgricultureProduction (economics)EcosystemAgricultural engineeringAgricultural productivityAgroforestryAgronomyBusinessEnvironmental scienceEcologyBiologyEngineeringEconomics

Abstract

fetched live from OpenAlex

Models of competition based on the Lotka-Volterra equation are introduced in this researching in order to develop an intelligent robot for a rice production ecosystem. These prediction equations are useful to estimate the competition between populations or biomasses of rice plants and weeds in different phases of the rice crop season and using this information about the superior plants, the robot will make decision about the appropriate timing for removing the snails in excess in paddy field; therefore snails remaining in the field can eat weed and rice plants will grow up with less competition.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.467

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.212
Teacher spread0.164 · 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 designBench or experimental
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

Citations6
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Faculty of Agriculture Kyushu UniversitySame topicSmart Agriculture and AIFrench-language works237,207