{"id":"W4240818985","doi":"10.4095/220090","title":"Investigation of multiscale product for change eetection in difference images","year":2004,"lang":"en","type":"report","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Product (mathematics); Computer science; Mathematics; 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":[0.0004499436,0.0001966013,0.0003971065,0.0003543306,0.00002243086,0.00001632593,0.00005587264,0.0003439786,0.000009701123],"category_scores_gemma":[0.0001547079,0.0001775925,0.00009576121,0.0002468913,0.0000186246,0.00009175296,0.00001118238,0.0002234943,0.000002925119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003900729,"about_ca_system_score_gemma":0.0001236482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003125889,"about_ca_topic_score_gemma":0.0004888655,"domain_scores_codex":[0.9987823,0.0000212217,0.0005151157,0.0002384391,0.0002846476,0.0001582877],"domain_scores_gemma":[0.9993845,0.00002904373,0.0001479421,0.0001930225,0.0002121129,0.00003339134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001385349,0.00008353911,0.006565132,0.01931231,0.0002525831,0.000009070463,0.001817497,0.005191708,0.3114319,0.00006931438,0.008925579,0.6462029],"study_design_scores_gemma":[0.00195016,0.0004193071,0.07376251,0.003339571,0.00008616985,0.00004663746,0.0001023133,0.003359183,0.9000233,0.000404359,0.01547857,0.001027887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8325343,0.006842477,0.05399603,0.0001496841,0.03753845,0.02045598,0.0006167258,0.002290769,0.04557554],"genre_scores_gemma":[0.9967414,0.0002044981,0.0003276263,0.000001817588,0.001027758,0.0004940996,0.00005279625,0.00004753874,0.001102448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.645175,"threshold_uncertainty_score":0.724201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1210395507331112,"score_gpt":0.2940163038918147,"score_spread":0.1729767531587035,"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."}}