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Record W4212992069 · doi:10.1515/9783110592566-201

Preface of the Editors

2019· book-chapter· en· W4212992069 on OpenAlexfundno aff
Nabil Derbel, Faouzi Derbel, Olfa Power, Smart Energies, Olfa Kanoun, Instrumentation Systems, Eng Derbel

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsnot available
FundersEidgenössisches NuklearsicherheitsinspektoratUniversität PaderbornUniversità degli Studi dell'AquilaUniversidade de VigoEidgenössische Technische Hochschule ZürichCentre National de la Recherche ScientifiqueUniversity of OttawaMassey UniversityPolitecnico di TorinoMonash UniversityTU Graz, Internationale Beziehungen und Mobilitätsprogramme
KeywordsHistory

Abstract

fetched live from OpenAlex

The tenth volume of the series "Advances in Systems, Signals and Devices" (ASSD), is a peer reviewed international scientific volume devoted to the field of sensors, circuits and instrumentation systems. The scope of the volume encompasses all aspects of research, development and applications of the science and technology in these fields. The topics include Sensors and measurement systems, optical sensors, chemical sensors, mechanical sensors, inductive sensors, capacitive sensors, microsensors, thermal sensors, biomedical and environmental sensors, fexible sensors, nano sensors, micro electronic systems, nano systems and nano technology, sensor signal processing, sensor interfaces, modeling, data acquisition, multi sensor data fusion, distributed measurements, device characterization and modeling, custom and semicustom circuits, analog circuit design, low-voltage, low-power VLSI design, circuit test, packaging and reliability, impedance spectroscopy, wireless sensors, wireless interfaces, wireless sensor networks, energy harvesting, circuits and systems design.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.197
Threshold uncertainty score0.661

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1970.188

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 head0.187
Teacher spread0.176 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2019
Admission routes1
Has abstractyes

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