MétaCan
Menu
Back to cohort
Record W4402351113 · doi:10.23919/jcc.2024.10670126

Intelligent Internet of Things with reliable communication and collaboration technologies

2024· article· en· W4402351113 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChina Communications · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsBrock University
Fundersnot available
KeywordsComputer scienceInternet of ThingsThe InternetWorld Wide WebComputer networkTelecommunicationsHuman–computer interactionMultimedia

Abstract

fetched live from OpenAlex

The Internet of Things (IoT) connects objects to Internet through sensor devices, radio frequency identification devices and other information collection and processing devices to realize information interaction. IoT is widely used in many fields, including intelligent transportation, intelligent healthcare, intelligent home and industry. In these fields, IoT devices connected via high-speed internet for efficient and reliable communications and faster response times. The application form of the intelligent IoT should be a multi-functional integrated IoT platform product, which has a high degree of intelligence and can carry out real-time monitoring of the IoT system. The intelligent IoT can continue to learn and evolve in an open environment, constantly meet the personalized needs of users and improve the service quality. However, this also imposes new security, privacy and energy consumption challenges, which highlights the need to develop novel collaborative methodologies to tackle these challenges.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.285

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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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