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Record W4383109361 · doi:10.47392/irjash.2023.s031

Survey on Smart Irrigation based on Smart Farming to yield profits using IOT Sensors

2023· article· en· W4383109361 on OpenAlexaff
Chitti T.N, Ayush Bajoria, Bagathi Ganesh, A. R. Harish, Sudeep Kudari

Bibliographic record

VenueInternational Research Journal on Advanced Science Hub · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsAgricultureIrrigationAgricultural engineeringProduction (economics)BusinessPopulationCropFood processingEnvironmental scienceAgroforestryAgricultural economicsGeographyEngineeringAgronomyEconomics

Abstract

fetched live from OpenAlex

International Research Journal on Advanced Science Hub (IRJASH) (eISSN 2582-4376) is an open access journal that was launched by RSP Science Hub in 2019. It aims to publish high quality peer reviewed articles to disseminate the progress in the field of science and technology therby bridging the gap between researchers, Academicians and industrial expert to remain up-to-date with the latest developments. Upon acceptance all the articles are free to read and download.

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.197
GPT teacher head0.412
Teacher spread0.215 · 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 designObservational
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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