{"id":"W2162667488","doi":"10.1109/ccece.1996.548202","title":"A method for error compensation for tactile sensors","year":2002,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Tactile sensor; Compensation (psychology); Computer science; Process (computing); Task (project management); Contact force; Modality (human–computer interaction); Artificial intelligence; Computer vision; Moment (physics); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009269009,0.001003063,0.0006746681,0.00101328,0.0006692453,0.0008966919,0.001446386,0.001718948,0.003673794],"category_scores_gemma":[0.00364492,0.0003488222,0.0005856507,0.0006965798,0.0005547776,0.001233492,0.0009526341,0.001396318,0.001445643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000510625,"about_ca_system_score_gemma":0.0005713399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001088458,"about_ca_topic_score_gemma":0.0008982928,"domain_scores_codex":[0.9986506,0.0001704132,0.00006664511,0.0002643669,0.0007890627,0.0000589537],"domain_scores_gemma":[0.9985952,0.000341169,0.0001108585,0.0002569599,0.0006600471,0.00003579093],"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":[0.0003769074,0.0000842366,0.0006226468,0.0003132155,0.0001049458,0.0002508457,0.0002362366,0.05282174,0.1166562,0.04172144,0.005154503,0.7816571],"study_design_scores_gemma":[0.00008389847,0.0002348628,0.00091285,0.00006781222,0.00007086999,0.001104931,0.00004086445,0.8624332,0.08711264,0.01334434,0.0344912,0.0001024935],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008240289,0.0001167459,0.9981917,0.00004068773,0.0000869289,0.00002119399,0.00000813,0.0003338242,0.0003767717],"genre_scores_gemma":[0.06940477,0.0002077915,0.9244485,0.0001012027,0.0001135418,0.0001212092,0.00006036223,0.0001160443,0.005426434],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003673794,"threshold_uncertainty_score":0.01229006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1490676508359105,"score_gpt":0.3730168804841877,"score_spread":0.2239492296482772,"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."}}