{"id":"W4407158731","doi":"10.1016/j.addma.2025.104692","title":"Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing","year":2025,"lang":"en","type":"article","venue":"Additive manufacturing","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Research Council Canada; Mitacs; Agency for Science, Technology and Research; McGill University","keywords":"Materials science; In situ; Audio visual; Modality (human–computer interaction); Transfer of learning; Knowledge transfer; Laser; Engineering drawing; Artificial intelligence; Optics; Computer science; Knowledge management; Multimedia; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009106771,0.000590175,0.0005416498,0.0006004488,0.0002608148,0.0006736774,0.0007924734,0.0008806087,0.002489552],"category_scores_gemma":[0.002331262,0.0001899109,0.0004765924,0.0005921476,0.0003735067,0.001415877,0.00124041,0.0008006216,0.0007069848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000264287,"about_ca_system_score_gemma":0.000356872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001283419,"about_ca_topic_score_gemma":0.001429536,"domain_scores_codex":[0.9995987,0.00009800878,0.00002070632,0.0001116143,0.0001148055,0.00005619606],"domain_scores_gemma":[0.9992895,0.0003964281,0.00005796933,0.00007450987,0.0001504466,0.00003109744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001019613,0.0005187828,0.001768832,0.0003945495,0.0001191186,0.0001715582,0.0002194177,0.06155323,0.1919565,0.00133552,0.001941887,0.7390009],"study_design_scores_gemma":[0.00001722948,0.0002142254,0.00423632,0.00003466412,0.0000659864,0.0002158211,0.00010966,0.8769611,0.1135738,0.002957751,0.001578981,0.00003452697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.154339,0.001371038,0.8374361,0.0002762871,0.0001890307,0.00008442222,0.0002953712,0.00189033,0.004118407],"genre_scores_gemma":[0.9114828,0.0005262491,0.08411816,0.0001565685,0.00008149202,0.00008409513,0.0002374011,0.0001207503,0.00319247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002489552,"threshold_uncertainty_score":0.008328438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009811584084861052,"score_gpt":0.2735026817450353,"score_spread":0.2636910976601743,"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."}}