{"id":"W4302556148","doi":"10.1007/978-3-319-41501-7","title":"Image Analysis and Recognition","year":2016,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Library science; Operations research; Engineering","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":[0.0004027438,0.0001854412,0.0002968888,0.000920292,0.00006670164,0.0001545351,0.000160667,0.0002471578,0.00002804505],"category_scores_gemma":[0.00003924803,0.00014474,0.00007225277,0.0008090228,0.0001292052,0.0001692713,0.00006159201,0.0002701928,0.00003628853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002023307,"about_ca_system_score_gemma":0.00006331219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001372169,"about_ca_topic_score_gemma":0.00003986821,"domain_scores_codex":[0.9988978,0.00001936088,0.0002261002,0.0003839301,0.0002557494,0.0002170672],"domain_scores_gemma":[0.9994024,0.0001656071,0.00005347523,0.0002459628,0.00007224491,0.00006031343],"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":[0.000002339856,0.00000176332,0.0000229191,0.00003064489,0.00003472694,0.000008613278,0.0000879741,0.002367853,0.0009831714,0.00000148519,0.0001146665,0.9963439],"study_design_scores_gemma":[0.001087359,0.0002811263,0.001098386,0.001443321,0.0003469248,0.0001017156,4.840592e-7,0.9274191,0.021047,0.03804582,0.007123129,0.002005666],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001332446,0.0001331543,0.9945317,0.00001376154,0.001151958,0.0001391937,0.00001322357,0.0001184991,0.002566067],"genre_scores_gemma":[0.8621639,0.0002946133,0.1273843,0.0003677604,0.007128824,0.00005138663,0.00006366469,0.0001751954,0.002370338],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9943382,"threshold_uncertainty_score":0.5902325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360716351404999,"score_gpt":0.2267060538329625,"score_spread":0.2130988903189125,"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."}}