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Record W2979481928 · doi:10.1109/jcsse.2019.8864201

The Control Model for Environmental Factor Effecting on Growth of St. John's Wort

2019· article· en· W2979481928 on OpenAlexaboutno aff
Narongsak Lekbangpong, Theera Srisawat, Apirat Wanichsombat, Jirapond Muangprathub

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGreenhouse Technology and Climate Control
Canadian institutionsnot available
Fundersnot available
KeywordsHypericum perforatumGreenhouseControl (management)Internet of ThingsWireless sensor networkPerennial plantComputer scienceWork (physics)Agricultural engineeringEngineeringWorld Wide WebArtificial intelligenceComputer networkMedicineHorticultureEcologyTraditional medicine

Abstract

fetched live from OpenAlex

St. John's wort (Hypericum perforatum L.) is a perennial herb and it has been used in medicine to treat various diseases. In terms of growth, it has required environmental factors and weather to appropriate. Thus, this paper presented the control model for environmental factor effecting on growth of St. John's wort by using two approaches. Those are the designed greenhouse and using Internet of Thing (IoT) and application. This work applied IoT as using the Wireless Sensor Network (WSN) and various sensors to measure, monitoring the change and control environmental factors in the greenhouse through the mobile and web application. This paper focused on three factor i.e. temperature, air humidity, and light. The proposed system was installed and tested in real situation at PSU farm. The result showed that the design and construction greenhouse can assist to control factors for the plant. The designed greenhouse can support to suitable adjust the factors of growth of St. John's wort. Moreover, the developed IoT and application can improve and controlled the factors better.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.165

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 studies0.0000.000
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 head0.010
GPT teacher head0.189
Teacher spread0.180 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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