{"id":"W4409726305","doi":"10.1016/j.measurement.2025.117629","title":"Novel method to automatize flash point detection in small volumes of liquid by computer vision using thermal images","year":2025,"lang":"en","type":"article","venue":"Measurement","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation","keywords":"Flash point; Flash (photography); Computer vision; Point (geometry); Thermal; Computer graphics (images); Artificial intelligence; Computer science; Optics; Physics; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001240414,0.0001235234,0.0001763194,0.0003033689,0.00006307597,0.00006845572,0.0002183988,0.00005071911,0.000006933007],"category_scores_gemma":[0.00003535612,0.0001208949,0.00006524631,0.0005570072,0.00001130989,0.0001798048,0.0001401188,0.00009520748,0.000009002806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000224417,"about_ca_system_score_gemma":0.00005503033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000179938,"about_ca_topic_score_gemma":0.0001296427,"domain_scores_codex":[0.9986944,0.0001531325,0.0003485597,0.000320227,0.0003105233,0.0001731496],"domain_scores_gemma":[0.9993605,0.0000324983,0.00009077213,0.0002233951,0.0002467045,0.00004610101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000276588,0.0001040385,0.00002042977,0.00002579497,0.000008540017,2.651649e-7,0.0001112444,0.0005403426,0.6561301,0.00001630511,0.0001226498,0.3428926],"study_design_scores_gemma":[0.0005586431,0.0002875076,0.003834047,0.0002424484,0.000007100585,0.000004662499,0.00001032471,0.2701997,0.7240278,0.0000843462,0.0006096151,0.0001337838],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09238134,0.00006693813,0.9062264,0.0001939832,0.0005602835,0.0002637441,0.00000203665,0.00008688217,0.0002183928],"genre_scores_gemma":[0.6887463,0.000001822857,0.3109647,0.0002125091,0.00002669495,0.0000194602,7.324818e-7,0.000005856622,0.00002195173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5963649,"threshold_uncertainty_score":0.4929952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04265314188275637,"score_gpt":0.296142500702722,"score_spread":0.2534893588199656,"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."}}