{"id":"W4234670702","doi":"10.4018/978-1-4666-0261-8.ch002","title":"On Abstract Intelligence","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Human intelligence; Computer science; Artificial intelligence; Artificial intelligence, situated approach; Abstraction; Marketing and artificial intelligence; Cognitive science; Intelligence cycle; Artificial general intelligence; Intelligent decision support system; Psychology; Military intelligence; Epistemology","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":[0.0008510566,0.0009498409,0.0005307015,0.001887011,0.001640837,0.005635067,0.0009276625,0.001777128,0.01823552],"category_scores_gemma":[0.002481859,0.0002966006,0.000611232,0.001917845,0.008273526,0.008948985,0.002489385,0.004059027,0.007284576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002993301,"about_ca_system_score_gemma":0.001510259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001564638,"about_ca_topic_score_gemma":0.001415394,"domain_scores_codex":[0.9990559,0.0002666194,0.00005324122,0.0001976727,0.0003392087,0.00008735801],"domain_scores_gemma":[0.9992263,0.0003387459,0.00004494014,0.0001634661,0.0001659673,0.00006059538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003100783,0.000004529954,0.00004201059,0.00005271244,0.000002159003,0.00001690749,0.000314336,0.0001268293,0.00004731407,0.958575,0.02541982,0.0153952],"study_design_scores_gemma":[0.00000305065,0.00000714716,0.00009661879,0.0001370187,0.000002463403,0.00008571314,0.0001265681,0.0002407184,0.00006855253,0.6252516,0.3739761,0.000004502489],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002060116,0.03513437,0.01925995,0.009940732,0.001940011,0.00005008236,0.0001839156,0.0001470467,0.9312837],"genre_scores_gemma":[0.2419225,0.07316791,0.02733634,0.01381051,0.006915472,0.0003746631,0.0009184339,0.0004776825,0.6350765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01823552,"threshold_uncertainty_score":0.06100392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03305277134912383,"score_gpt":0.2640267894660489,"score_spread":0.230974018116925,"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."}}