Digital TILSAM systems – providing FAIR data and SI traceability to smart sensor networks for air quality monitoring
Bibliographic record
Abstract
The TILSAM method [1] offers a route for in situ calibrations of sensors delivering SI traceable amount fraction results of molecular gas species. Its feasibility to underpin Optical Gas Standards (OGSs) has been demonstrated in a number of cases and shown to be complementary to conventional reference material-based gas standards [2,3]. The method has advantages in online measurements and for quantifying unstable gas mixtures [4,5]. In a recent project outline [6] a digital version of the TILSAM method has been discussed, where the OGS in a fully autonomous version provides harmonised FAIR (Findable-Accessible-Interoperable-Reusable) data outcomes, that are machine-readable and will be referenced to machine-readable data from the CIPM KCDB [7] and the IMRR [8]. For this, the FAIRification process [9] is followed, asserting findability and accessibility of the digital TILSAM results. A novel semantic model for OGSs, following Semantic Sensor Network Ontology [10], to facilitate the interoperability and reusability of the data will be discussed. By means of the digital TILSAM a sensor network as a case study also introduces key aspects for enduring a System of Systems (SOS) [11] approach for monitoring network applications. The resulting OGSs based on the digital TILSAM method can serve as a gold standard for in situ calibrations of smart sensor networks. Particularly, OGSs realized on NO2, NH3 or HCl [2] will be most useful in air quality monitoring applications. In this presentation, we describe the proposed application of the TILSAM method to sensor networks and provide an example of its digital transformation providing FAIR data, following the created semantic model for air quality monitoring applications. [1] Traceable Infrared Laser-Spectrometric Amount fraction Measurement (TILSAM), TILSAM technical protocol V.1, EURAMET project 934, URL: https://www.euramet.org/Media/docs/projects/934_METCHEM_Interim_Report.pdf [2] J.A. Nwaboh et al., “Optical gas standards for reactive gas species concentration measurements based on dTDLAS”, Optical Sensors 2020, URL: https://doi.org/10.1364/es.2020.ew4h.2 [3] J.A. Nwaboh et al., “Towards an optical gas standard for traceable calibration-free and direct NO2 concentration measurements”, Applied Sciences 11 (2021) 5361, U https://doi.org/10.3390/app11125361 [4] J.C. Petersen, O. Werhahn, “Progressing laser spectroscopic methods as future Optical Gas Standards – an option for calibrations of smart sensor networks”, submitted to Applied Optics Feature Issue OPTICA conference papers Laser Applications to Chemical, Security and Environmental Analysis, paper ID LTh5E.2, Vancouver, 2022 [5] “Comparison on 100 µmol/mol HCl in nitrogen”, EURAMET TC-MC project 1498, final report, https://www.euramet.org/technical-committees/tc-mc/tc-mc-projects/ [6] “Challenges and opportunities in sensor network metrology”, EURAMET TC-IM Project number 1551, URL: https://www.euramet.org/technical-committees/interdisciplinary-metrology/tc- im-projects/details/project/challenges-and-opportunities-in-sensor-network-metrology/ [7] The CIPM Key Comparison Data Base (KCDB), Bureau International des Poids et Mesures (BIPM), URL: https://www.bipm.org/kcdb/ [8] The International Metrology Resource Registry (IMRR), Bureau International des Poids et Mesures (BIPM), URL: http://imrr.bipm.org/ [9] GO FAIR Initiative. FAIRification Process, URL: https://www.go-fair.org/fair- principles/fairification-process/ [10] Semantic Sensor Network Ontology. W3C recommendation, URL: https://www.w3.org/TR/vocab-ssn/ [11] System of Systems. KIT IPEK Glossar, URL: https://www.ipek.kit.edu/glossar/index.php?title=System_of_Systems
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".