{"id":"W7116334614","doi":"10.1016/j.aei.2025.104238","title":"Linking microstructure informatics with characterization knowledge in additively manufactured composites through customized and hybrid vision-language representations for automated qualification","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada","funders":"Fonds de recherche du Québec – Nature et technologies; National Research Council Canada","keywords":"Interpretability; Visualization; Encoder; Bottleneck; Collocation (remote sensing); Characterization (materials science); Segmentation; Normalization (sociology); Similarity (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":[0.0003376056,0.0002371281,0.0003171167,0.0002353659,0.0001718125,0.0002567873,0.000238459,0.00007434318,0.00001110586],"category_scores_gemma":[0.0001731378,0.0002094072,0.00002442237,0.0003509258,0.0000722832,0.001328819,0.00008706719,0.0001471188,0.000007304321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008682646,"about_ca_system_score_gemma":0.00005404948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005902453,"about_ca_topic_score_gemma":0.000004078226,"domain_scores_codex":[0.9985859,0.0000332325,0.0007721445,0.0001553599,0.0001539634,0.0002993309],"domain_scores_gemma":[0.9989113,0.0002972415,0.0003489137,0.0002723075,0.0001281151,0.0000420889],"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":[0.0001024321,0.00002964265,0.0001098027,0.0009422296,0.00001027057,7.009419e-7,0.01108845,0.3857327,0.5984435,0.0007099766,0.00004038778,0.002789937],"study_design_scores_gemma":[0.001483295,0.00005921996,0.00672011,0.0005078554,0.00001847573,0.00001143732,0.000719111,0.8571846,0.1310454,0.00004271064,0.001938204,0.0002695916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5973023,0.00002223697,0.4011387,0.000031468,0.0002104069,0.0006402452,0.0001175087,0.0004370167,0.0001001556],"genre_scores_gemma":[0.5395421,0.00001953617,0.4594618,0.00009409423,0.00001865016,0.0001510478,0.000655333,0.00001892959,0.00003849409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4714519,"threshold_uncertainty_score":0.8539377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003567290540910259,"score_gpt":0.2762246485175267,"score_spread":0.2726573579766164,"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."}}