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Record W4399870570 · doi:10.23977/acss.2024.080116

Design of ZigBee IoT System in Smart Agricultural Greenhouses

2024· article· en· W4399870570 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

VenueAdvances in Computer Signals and Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsnot available
Fundersnot available
KeywordsInternet of ThingsGreenhouseAgricultureEmbedded systemComputer scienceAgricultural engineeringEngineeringHorticultureGeographyBiology

Abstract

fetched live from OpenAlex

Zigbee has been widely used by scholars and technicians in China, it's a currently circulating solution on digital agricultural technology. To tackle issues in traditional agricultural management methods and enhance supervision, data accuracy, and efficiency, a proposed smart agricultural monitoring system uses ZigBee wireless technology and the Internet of Things (IoT). This system employs a tree network topology, distributing monitoring nodes throughout fields and greenhouses. These nodes gather data through wireless sensors, transmitting it for real-time analysis on a central server. The design optimizes power usage, reduces costs, and ensures precise control, summarizing parameters like the maximum network capacity under varying network scan times. Ultimately, it significantly improves crop growth conditions. It is of great significance to regulate the growth environment of crops to meet the growth needs of crops, thereby improving the yield and quality of crops.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.489

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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