{"id":"W2774365934","doi":"10.1109/smc.2017.8123119","title":"Building cognitive knowledge bases sharable by humans and cognitive robots","year":2017,"lang":"en","type":"article","venue":"","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cognitive computing; Interfacing; Embodied cognition; Cognition; Knowledge base; Set (abstract data type); Cognitive model; Comprehension; Cognitive robotics; Artificial intelligence; Semantic network; Human–computer interaction; Programming language; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001882697,0.0005602649,0.0006965963,0.001610475,0.001034,0.002626456,0.002453554,0.000971965,0.002896175],"category_scores_gemma":[0.007711043,0.0006707624,0.0008805895,0.001425681,0.001640094,0.0067676,0.004602063,0.001673368,0.0008374198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007752307,"about_ca_system_score_gemma":0.001717811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003120174,"about_ca_topic_score_gemma":0.004091504,"domain_scores_codex":[0.9989862,0.0002249061,0.00008256491,0.0002424638,0.0003722907,0.00009158979],"domain_scores_gemma":[0.9957651,0.001342403,0.0002898213,0.001829527,0.0005559951,0.0002170512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002856287,0.0003618897,0.002990417,0.0004535247,0.0003233058,0.0007620419,0.002072467,0.1246336,0.02165454,0.397626,0.00807478,0.4407618],"study_design_scores_gemma":[0.00006349847,0.0001137327,0.001099734,0.0001191069,0.0001388856,0.0002467562,0.000794731,0.5066206,0.01421314,0.4454464,0.03107833,0.00006519749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0576508,0.0003363955,0.9319906,0.000472365,0.00008822578,0.0002216833,0.000332629,0.001717044,0.007190311],"genre_scores_gemma":[0.3682483,0.0004473223,0.6256781,0.0002163342,0.00004982183,0.0003283697,0.001439135,0.0002283595,0.003364247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003120174,"threshold_uncertainty_score":0.009956777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03135629234654067,"score_gpt":0.3145465314676632,"score_spread":0.2831902391211225,"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."}}