{"id":"W2122383406","doi":"10.1109/ical.2009.5262575","title":"Optimized planar grasping synthesis algorithm for multi-fingered robotic hand","year":2009,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"GRASP; Computer science; Planar; Set (abstract data type); Rank (graph theory); Measure (data warehouse); Algorithm; Field (mathematics); Artificial intelligence; Computer vision; Mathematics; Computer graphics (images); Data mining","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":[0.0000926268,0.0001328946,0.0001954458,0.00009506966,0.0001184415,0.00007908792,0.00007808915,0.00006646826,0.00007871521],"category_scores_gemma":[0.00005929108,0.0001300446,0.00007593568,0.00008652328,0.000008550285,0.0001007593,0.000003235361,0.00008189876,0.00003404313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003024619,"about_ca_system_score_gemma":0.000004935791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006749432,"about_ca_topic_score_gemma":0.000002194279,"domain_scores_codex":[0.999357,0.00001298668,0.0001856476,0.0001398981,0.00007230649,0.0002321092],"domain_scores_gemma":[0.9996846,0.00009093076,0.00002244902,0.0001142914,0.00002257633,0.00006520419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003757557,0.00001151347,0.000008594248,0.00001353337,0.00002193401,0.000001732181,0.00008079975,0.9436458,0.001238233,0.00009464249,0.0003687661,0.05451067],"study_design_scores_gemma":[0.0006000536,0.00001833482,0.00103942,0.00003519209,0.00002053638,0.000002963417,0.00004311363,0.9959706,0.001822326,0.0000224173,0.0002403699,0.0001846552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002961177,0.0001369319,0.9978741,0.00009049667,0.0001630301,0.0001960574,2.271777e-7,0.0004349378,0.0008081309],"genre_scores_gemma":[0.3497609,0.00001285973,0.6492937,0.0001061617,0.00008226634,0.00002286983,0.000008086316,0.00002955476,0.0006835968],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3494648,"threshold_uncertainty_score":0.5303064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04376312300966932,"score_gpt":0.2614301486621817,"score_spread":0.2176670256525123,"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."}}