{"id":"W2352827293","doi":"","title":"Defects segmentation for wood floor based on image fusion method","year":2014,"lang":"en","type":"article","venue":"Dianji yu kongzhi xuebao","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Image stitching; Image fusion; Image segmentation; Pattern recognition (psychology); Computer science; Margin (machine learning); Mathematics; Image (mathematics)","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":[0.0008118291,0.0002302935,0.0002766766,0.0002267641,0.000128556,0.0001078519,0.00009548194,0.0002180887,0.00006400541],"category_scores_gemma":[0.0002246004,0.0002165297,0.0001735387,0.0002525045,0.000009694284,0.0001598696,0.00001086708,0.0001625687,0.0001722753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000106743,"about_ca_system_score_gemma":0.00001757549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003905458,"about_ca_topic_score_gemma":0.00001401412,"domain_scores_codex":[0.9986907,0.0001399428,0.0003147838,0.0002924323,0.0002424886,0.0003196296],"domain_scores_gemma":[0.9991106,0.0003169368,0.00007051283,0.0003086619,0.00007767975,0.0001155712],"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":[0.000503333,0.0001549277,0.0001575582,0.0005841145,0.00009184047,0.000006770384,0.0004349579,0.09024925,0.5551211,0.000830068,0.02528935,0.3265767],"study_design_scores_gemma":[0.004143826,0.0006019726,0.0006675915,0.0001510796,0.00006295382,0.000003553719,0.00009202749,0.5770527,0.3772241,0.000267605,0.03919197,0.0005407294],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1618474,0.00002492283,0.8271477,0.0000598127,0.002834393,0.001093625,0.00003302593,0.0008687923,0.006090302],"genre_scores_gemma":[0.9840675,0.000002225207,0.01428978,0.0001803888,0.0009192714,0.0001967294,0.00006185716,0.0001152206,0.0001670024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8222201,"threshold_uncertainty_score":0.8829826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01319994454038338,"score_gpt":0.2609985168343384,"score_spread":0.2477985722939551,"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."}}