{"id":"W2159987694","doi":"10.1109/robot.2005.1570769","title":"Incremental Learning for Mapping Image Variations to Actions","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Visual servoing; Artificial intelligence; Computer vision; Computer science; Image (mathematics); Feature (linguistics); Position (finance); Motion (physics); Robot; Function (biology); Space (punctuation); Feature vector","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":[0.00009132367,0.00005018375,0.00004637736,0.00009464986,0.0002953189,0.0001289433,0.0001669001,0.000009800318,0.00003405039],"category_scores_gemma":[0.00004251606,0.00004943443,0.00002976927,0.0002442863,0.000005376024,0.0005394911,0.0000999324,0.00005032638,0.00007254317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003672607,"about_ca_system_score_gemma":0.00001363379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004293739,"about_ca_topic_score_gemma":0.000007352428,"domain_scores_codex":[0.9994863,0.00001059108,0.0001070002,0.000168286,0.00007674922,0.000151086],"domain_scores_gemma":[0.9997182,0.00005348496,0.00001732665,0.0001209229,0.00004998664,0.00004005121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002734482,0.00009595638,0.0007284873,0.000007540265,0.000008700364,0.000001587595,0.0004507778,0.004295433,0.3835811,0.4091038,0.01706839,0.1846556],"study_design_scores_gemma":[0.0003641232,0.00004016447,0.006659825,0.00001137191,0.000002185964,0.000006007043,0.0002062766,0.7284892,0.01271604,0.006449148,0.2448446,0.0002110281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004701241,0.000002823544,0.9762662,0.002165571,0.0001162416,0.0001293379,5.308422e-7,0.0002068876,0.02064225],"genre_scores_gemma":[0.2026261,3.259755e-7,0.7946296,0.0004374002,0.00005366994,0.0000215349,0.000002465584,0.000004036937,0.002224815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7241938,"threshold_uncertainty_score":0.2271384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410235215399673,"score_gpt":0.3071534386167402,"score_spread":0.2830510864627435,"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."}}