{"id":"W3087326566","doi":"10.1016/j.sna.2020.112339","title":"Modeling partition dynamics through multiple interfaces and characterization by wavelength modulation spectroscopy","year":2020,"lang":"en","type":"article","venue":"Sensors and Actuators A Physical","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Desorption; Sorption; Dissolved gas analysis; Phenomenological model; Acetylene; Wavelength; Characterization (materials science); Spectroscopy; Analytical Chemistry (journal); Chemical physics; Modulation (music); Materials science; Chemistry; Biological system; Adsorption; Transformer; Nanotechnology; Physical chemistry; Optoelectronics; Chromatography; Physics; Transformer oil; Organic chemistry; Acoustics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001558296,0.0001542292,0.0001658961,0.00000924191,0.000137204,0.00006663482,0.00004408307,0.00006243076,0.00002639063],"category_scores_gemma":[0.00001638665,0.0001488223,0.00002858355,0.00007282002,0.00005011717,0.0002285332,0.00003123491,0.0001481466,0.00000543648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002962322,"about_ca_system_score_gemma":0.000006933288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002610365,"about_ca_topic_score_gemma":0.000003573135,"domain_scores_codex":[0.9992554,0.00001051473,0.0001443699,0.0003253558,0.0001049374,0.0001594328],"domain_scores_gemma":[0.9997059,0.00003084,0.00005292615,0.0000982434,0.00002059339,0.00009156355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006248397,0.00009821961,0.0005829112,0.00005335271,0.00002292425,4.487181e-7,0.002179059,0.0003055746,0.9895617,0.001702725,0.00003486783,0.005395679],"study_design_scores_gemma":[0.0002305964,0.00002740136,0.00008314593,0.00001123829,0.00001999749,8.930683e-7,0.000292314,0.8428814,0.1554538,0.0007548123,0.0001071743,0.0001372437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650545,0.000008784385,0.03370739,0.0007985494,0.00001135504,0.00007954732,0.0001255933,0.0000892715,0.0001250321],"genre_scores_gemma":[0.998766,0.00008005295,0.0003502469,0.0001076775,0.0002037104,0.00001441129,0.0004410332,0.00002177835,0.00001506859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8425758,"threshold_uncertainty_score":0.6068797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125606531492772,"score_gpt":0.237718617241205,"score_spread":0.2264625519262773,"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."}}