MétaCan
Menu
Back to cohort

Usage of single-chip computers for data collection and control

2016· article· en· W2460097099 on OpenAlexfundno aff
Jiří Czebe, Jaromír Škuta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Sensor Networks for Data Analysis
Canadian institutionsnot available
FundersCanadian Patient Safety Institute
KeywordsComputer scienceMicrocontrollerInitializationEmbedded systemTask (project management)Control systemReset (finance)Interface (matter)Computer hardwareControl (management)SCADAData collectionOperating systemEngineering

Abstract

fetched live from OpenAlex

This paper deals with usage of single-chip computers for collection of data and control. The aim is to design a general control system, which is easy to use and easy to implement for other laboratory tasks. Performance of control system is limited by performance of used MCU (PIC 16F873A) and its peripherals (2xPWM, 5xA/D...). Main task of MCU are communication with upper level and implementation of required control action together with data collection. Communication between control system (IPC) and MCUs is provided by serial interface. In the first step control system defines initialization of MCU (reset needed) for desired laboratory task then continues with the control application. Everything is controlled and monitored from SCADA/HMI system called Control Web. Application is accessible via Internet browser in local network.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.037
GPT teacher head0.242
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicWireless Sensor Networks for Data AnalysisFrench-language works237,207