{"id":"W1983271565","doi":"10.1117/12.386857","title":"<title>Automatic inspection using correlation of range images</title>","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National d'Optique","funders":"","keywords":"Correlation; Coding (social sciences); Range (aeronautics); Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Mathematics; Statistics; Engineering; Geometry","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":[0.0002165256,0.0001627793,0.0002280613,0.00006116324,0.00003434329,0.00003193177,0.00023953,0.0001229457,0.0002229401],"category_scores_gemma":[0.0001061913,0.0001468405,0.0002360774,0.0002142721,0.00007500513,0.0002333315,0.00002372967,0.0001440241,0.00002120217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001199582,"about_ca_system_score_gemma":0.00001154939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002121697,"about_ca_topic_score_gemma":4.543203e-8,"domain_scores_codex":[0.9988924,1.568574e-8,0.0003773112,0.0001448474,0.0003977199,0.0001877644],"domain_scores_gemma":[0.9993821,0.00003772505,0.00009604483,0.00003906457,0.0003937324,0.00005137773],"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":[0.00005277677,0.0001687636,0.0005295197,0.001341699,0.0006538725,2.009928e-7,0.0002287875,0.006775014,0.7772993,0.1884629,0.01439017,0.010097],"study_design_scores_gemma":[0.001126842,0.0001512266,0.002519258,0.0007001621,0.000273397,0.00001448408,0.0001723787,0.9198042,0.06059157,0.00144085,0.01266442,0.0005412093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9558195,0.0002011755,0.0001441291,0.00007369723,0.0003189934,0.0002101094,0.00001767943,0.0001274846,0.04308721],"genre_scores_gemma":[0.9479506,0.0001742285,0.05106655,0.00001587003,0.0002702338,0.00002938292,0.000005498352,0.00006973795,0.0004178991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9130292,"threshold_uncertainty_score":0.5987982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336269699615562,"score_gpt":0.2205991075810476,"score_spread":0.207236410584892,"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."}}