{"id":"W4405360455","doi":"10.1115/ipc2024-120952","title":"In-Situ Concentration Measurement of Blended Hydrogen Gas Using Sensor Fusion Enhanced by Machine Learning Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"In situ; Hydrogen; Fusion; Materials science; Sensor fusion; Computer science; Optoelectronics; Artificial intelligence; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077232,0.0007483092,0.0006361837,0.0005195881,0.0002646074,0.0005449028,0.0005629894,0.0006920723,0.0004564754],"category_scores_gemma":[0.001037563,0.0002286322,0.0007307617,0.0005712123,0.0002335978,0.0007810236,0.0003736156,0.0006462966,0.0001908225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006807045,"about_ca_system_score_gemma":0.0004313132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008693466,"about_ca_topic_score_gemma":0.005591698,"domain_scores_codex":[0.9997115,0.00005478854,0.00001815008,0.0001118977,0.00006887191,0.00003489786],"domain_scores_gemma":[0.99963,0.0001760926,0.00004397878,0.00003642719,0.0001022198,0.00001124635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002408425,0.0002844525,0.009681647,0.0001009918,0.0001193077,0.00007367389,0.00008062131,0.8824426,0.02072721,0.0004435568,0.000756765,0.08504838],"study_design_scores_gemma":[0.000001541588,0.00001732169,0.0006171085,0.000001343261,0.000004161692,0.000004206663,0.000004566995,0.9960336,0.003171889,0.00008201606,0.00005857769,0.000003685698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5967309,0.0005212073,0.3985156,0.0002125316,0.00008125039,0.00006013421,0.0003761295,0.001331799,0.00217042],"genre_scores_gemma":[0.9793198,0.0000662872,0.01987926,0.00002719788,0.000007571533,0.00002914464,0.0002153609,0.000012689,0.0004426474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008693466,"threshold_uncertainty_score":0.0172857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01741061026260713,"score_gpt":0.2337050719251882,"score_spread":0.216294461662581,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}