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Record W4399348762 · doi:10.34256/ijcci2323

Dual Axis Solar Tracking of Solar Radiation for Agriculture usage

2023· article· en· W4399348762 on OpenAlexaff
Monika Gupta, Swati Nigam, Sonika Katta, Vivek Upadhyay, K. Ashok, G. S. Sharma

Bibliographic record

VenueInternational Journal of Computer Communication and Informatics · 2023
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsTracking (education)RadiationSolar trackerEnvironmental scienceDual (grammatical number)Solar energyOpticsPhysicsEngineeringElectrical engineeringSociologyArt

Abstract

fetched live from OpenAlex

Energy is one of the important parts of our life. As there is decline in fossil fuels and increasing demand for energy an alternate energy source is required which is renewable energy source like solar, wind etc. So, we use solar panels which trap the energy from the sun and produce electricity, and this energy is used for agriculture purpose like to run water pumps and to meet other energy requirements in agriculture. Due to rotation of earth the stationery solar panel will receive energy only for smaller duration so to overcome this we use dual axis tracking system which rotates solar panel according to direction of sun and helps in producing more solar energy. Agriculture is one of the major contributing sectors to the economy of a country and it requires automation and advanced technology so that it helps farmers in producing more yield and better crops. So, in agriculture continuous monitoring of soil and water level is required so we can automate this which helps the farmers where the device continuously monitors and depending upon the moisture level of the soil the water pumps get on automatically and we can use this for different crops and set threshold depending upon the crop type. And we can also integrate this idea with IOT technology for improvements. By this we create sustainable energy indirectly producing sustainable environment.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.277
Teacher spread0.256 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2023
Admission routes1
Has abstractyes

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