{"id":"W2001319303","doi":"10.1145/638750.638771","title":"On the Latest Development in Cognitive Informatics","year":2003,"lang":"en","type":"article","venue":"ACM SIGSOFT Software Engineering Notes","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Engineering informatics; Informatics; Business informatics; Computer science; Cognitive computing; Cognition; Data science; Health Administration Informatics; Cognitive ergonomics; Domain (mathematical analysis); Health informatics; Cognitive science; Software engineering; Knowledge management; Engineering; Psychology; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0004617474,0.0001977766,0.0001447175,0.000126646,0.0001129049,0.0001037163,0.0005948468,0.00006382552,0.00001116034],"category_scores_gemma":[0.09214576,0.0001564264,0.00003707016,0.0005126481,0.00001798035,0.0001390952,0.0002173064,0.0003254789,0.00007352121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004826014,"about_ca_system_score_gemma":0.00007048484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002011545,"about_ca_topic_score_gemma":0.000002359055,"domain_scores_codex":[0.9989403,0.00003483039,0.0002723298,0.0001872956,0.000206168,0.0003590318],"domain_scores_gemma":[0.9245246,0.0747874,0.00007104802,0.0004399029,0.0001018646,0.00007519875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000342804,0.0006535292,0.4508664,0.0002630171,0.0002558428,0.0001823838,0.03186525,0.09711829,0.00008444716,0.07378729,0.002579909,0.3423094],"study_design_scores_gemma":[0.00523728,0.0007730628,0.8193147,0.007093844,0.00005591524,0.0002026977,0.0004434455,0.08500284,0.04464142,0.004768388,0.02678293,0.005683434],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5339631,0.0001483749,0.4646945,0.0001377722,0.0003374091,0.0002051593,0.00000126474,0.0004316891,0.00008069229],"genre_scores_gemma":[0.9531603,0.00000511242,0.0463352,0.0004207461,0.00002010792,0.00002695347,0.000003163681,0.00001367737,0.0000147524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4191972,"threshold_uncertainty_score":0.9155015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176611561208452,"score_gpt":0.2268775306799315,"score_spread":0.205111415067847,"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."}}