{"id":"W2023342744","doi":"10.1109/ccece.2010.5575231","title":"Visual sorting of recyclable goods using a support vector machine","year":2010,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Redeemer University","funders":"","keywords":"Sorting; Support vector machine; Computer science; Artificial intelligence; Histogram; Computer vision; Scale (ratio); Rotation (mathematics); Contextual image classification; Pattern recognition (psychology); Image (mathematics); Geography; Cartography","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":[0.0003372562,0.00007962563,0.0001211951,0.0001092059,0.00007367581,0.00005516895,0.0003002791,0.00005631076,0.0002052875],"category_scores_gemma":[0.00005182983,0.00006936711,0.00005312839,0.0002907661,0.00002634539,0.0003942093,0.0001470769,0.0001661367,0.00001045432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001182209,"about_ca_system_score_gemma":0.00006338806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004023662,"about_ca_topic_score_gemma":0.00005205898,"domain_scores_codex":[0.9992443,0.00001895257,0.0002283198,0.0001850438,0.0001463395,0.0001770435],"domain_scores_gemma":[0.9994774,0.00002613631,0.0001059273,0.0002825167,0.000064184,0.0000437909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003680958,0.00006095704,0.001149462,0.00001499356,0.000007906016,0.000008239225,0.0001427099,0.000001555437,0.9164143,0.003006492,0.0002273985,0.07896226],"study_design_scores_gemma":[0.00007644025,0.00009781664,0.0003222292,0.00000385634,0.000002665167,0.00003720163,0.000005782379,0.0335958,0.9643623,0.0006500075,0.0007543124,0.00009158387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1140543,0.000005998376,0.8680764,0.00007096527,0.0003564935,0.00009202122,4.989795e-7,0.0004306204,0.01691268],"genre_scores_gemma":[0.8060867,8.88663e-7,0.1932752,0.0001187359,0.00004372653,0.000002229298,3.645707e-7,0.000006175322,0.0004660072],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6920325,"threshold_uncertainty_score":0.2828709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01251176957968931,"score_gpt":0.293109511181964,"score_spread":0.2805977416022747,"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."}}