15th International Workshop on Advanced Infrared Technology and Applications (AITA)
Bibliographic record
Abstract
first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing: Column Width: Background: Open AccessEditorial 15th International Workshop on Advanced Infrared Technology and Applications (AITA) † by Paolo Bison 1, Mario D'Acunto 2, Xavier Maldague 3, Davide Moroni 4,*, Valentina Raimondi 5, Antoni Rogalski 6, Takahide Sakagami 7 and Marija Strojnik 8 1 Istituto per le Tecnologie della Costruzione, CNR, 35127 Padova, Italy 2 Istituto di Biofisica, CNR, 56124 Pisa, Italy 3 Electrical and Computer Engineering Department, Université Laval, Quebec, QC G1V 0A6, Canada 4 Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", CNR, 56124 Pisa, Italy 5 Istituto di Fisica Applicata "Nello Carrara", CNR, 50019 Sesto Fiorentino FI, Italy 6 Institute of Applied Physics, Military University of Technology, 01-476Warsaw, Poland 7 Department of Mechanical Engineer, Kobe University, Kobe 657-8501, Japan 8 Centro de Investigaciones en Óptica (CIO), 37150 León, Mexico * Author to whom correspondence should be addressed. † Presented at the 15th International Workshop on Advanced Infrared Technology and Applications (AITA 2019), Florence, Italy, 17–19 September 2019. Proceedings 2019, 27(1), 53; https://doi.org/10.3390/proceedings2019027053 Published: 16 December 2019 (This article belongs to the Proceedings of The 15th International Workshop on Advanced Infrared Technology and Applications) Download Download PDF Versions Notes Abstract The 15th International Workshop on Advanced Infrared Technology and Applications has been held in Florence on 16–19 September 2019. Keywords: Infrared imaging; Smart and fiber-optic sensors; Thermo-fluid dynamics; Biomedical applications; Environmental monitoring; Aerospace and industrial applications; Nanophotonics and nanotechnologies; Astronomy and Earth observation; Non-destructive tests and evaluation; Systems and applications for the cultural heritage Infrared imaging; Smart and fiber-optic sensors; Thermo-fluid dynamics; Biomedical applications; Environmental monitoring; Aerospace and industrial applications; Nanophotonics and nanotechnologies; Astronomy and Earth observation; Non-destructive tests and evaluation; Systems and applications for the cultural heritage Share and Cite MDPI and ACS Style Bison, P.; D'Acunto, M.; Maldague, X.; Moroni, D.; Raimondi, V.; Rogalski, A.; Sakagami, T.; Strojnik, M. 15th International Workshop on Advanced Infrared Technology and Applications (AITA). Proceedings 2019, 27, 53. https://doi.org/10.3390/proceedings2019027053 AMA Style Bison P, D'Acunto M, Maldague X, Moroni D, Raimondi V, Rogalski A, Sakagami T, Strojnik M. 15th International Workshop on Advanced Infrared Technology and Applications (AITA). Proceedings. 2019; 27(1):53. https://doi.org/10.3390/proceedings2019027053 Chicago/Turabian Style Bison, Paolo, Mario D'Acunto, Xavier Maldague, Davide Moroni, Valentina Raimondi, Antoni Rogalski, Takahide Sakagami, and Marija Strojnik. 2019. "15th International Workshop on Advanced Infrared Technology and Applications (AITA)" Proceedings 27, no. 1: 53. https://doi.org/10.3390/proceedings2019027053 Find Other Styles Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here. Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.051 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".