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Record W4387401608 · doi:10.59934/jaiea.v3i1.372

Application of Case Based Reasoning Method to Diagnose Rice Plant Diseases

2023· article· en· W4387401608 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2023
Typearticle
Languageen
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsRice plantOryza sativaBiologyAgronomyBlightSowingLeaf spotBiotechnology

Abstract

fetched live from OpenAlex

Rice (Latin: Oryza sativa) is one of the most important cultivated crops in civilization. Although it mainly refers to a type of cultivated plant, rice is also used to refer to several types of the same genus (genus), commonly referred to as wild rice. The problem that often arises is that many rice plants are susceptible to pests and diseases during the planting period. Some pests and diseases that can attack rice plants include: leaf blight, grass, tongguo, rice spout, and dwarf grass. Generally, when rice plants are attacked by pests and diseases, farmers will immediately use pesticides or treatment methods that are sometimes not in accordance with pests. As a result, treatment is not optimal and can even cause new pests and diseases. The purpose of this study is to assist farmers in identifying early symptoms of plant diseases and pests of rice plant diseases using the case base reasoning method, so that the treatment of plant diseases and insect pests is more concentrated and maximal.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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