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Record W2061038353 · doi:10.5539/cis.v7n4p21

Geographic Information System for Detecting Spatial Connectivity Brown Planthopper Endemic Areas Using a Combination of Triple Exponential Smoothing - Getis Ord

2014· article· en· W2061038353 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

VenueComputer and Information Science · 2014
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsExponential smoothingComputer scienceClass (philosophy)Geographic information systemComponent (thermodynamics)Spatial analysisSmoothingWarning systemData miningPreprocessorData scienceCartographyArtificial intelligenceGeographyRemote sensingComputer vision

Abstract

fetched live from OpenAlex

This study aims to develop a GIS application to detect the possible formation of brown planthoppers (BPH) (Nilaparvata lugens.Stal) endemic areas based on spatial trend, hierarchical effects and risks areas caused of spatial connectivity in a particular area. The study was conducted through five stages: (1) the collection and preprocessing of research data, (2) database development, (3) the creation of the component class Exponential Smoothing, Weight Metrics and Getis Ord, (4) development of a Early Warning class and GIS applications, and (5) information visualization in the form of graphs, maps and tables. The results show that the software component in this study; the class prediction engine; Getis Ord class and class early detection function optimally generate predictive, endemic regions and early warning information on the period ahead.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.979
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.045
GPT teacher head0.318
Teacher spread0.273 · 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