{"id":"W4402709544","doi":"10.62477/jkmp.v24i3.445","title":"Bringing Artificial Intelligence to Research Analytics: Research Highlighter-MatchMaker","year":2024,"lang":"en","type":"article","venue":"Journal of Knowledge Management and Practice","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; Data science; Computer science; Engineering ethics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01479578,0.0002062303,0.0002735892,0.003012208,0.0004649645,0.002786903,0.0007356785,0.00008337309,0.0003963568],"category_scores_gemma":[0.001907241,0.0001706894,0.00008959899,0.004007014,0.0001782919,0.003597821,0.001125808,0.0009859884,0.002335518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001099637,"about_ca_system_score_gemma":0.00006321981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006501665,"about_ca_topic_score_gemma":0.00004324357,"domain_scores_codex":[0.9968578,0.0001787673,0.0007296111,0.0004508147,0.00116319,0.0006198736],"domain_scores_gemma":[0.9956684,0.001700824,0.0001661112,0.0003473358,0.002047408,0.00006999469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000418101,0.0002975983,0.00005466749,0.00126968,0.000232186,0.0006696859,0.0005780573,0.00007207756,0.0001821544,0.5040551,0.1241232,0.3680475],"study_design_scores_gemma":[0.00003838788,0.00006173972,0.0001035095,0.0008536546,0.0001540619,0.00007641642,0.00363686,0.003892135,0.0002056818,0.02392627,0.9668434,0.0002079348],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.009867365,0.02392061,0.1290519,0.0572041,0.007459673,0.001312177,0.00000598278,0.0001949184,0.7709833],"genre_scores_gemma":[0.9837799,0.001729813,0.003243276,0.0004207977,0.005960741,0.00001561974,0.000004067924,0.00005695629,0.0047888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9739125,"threshold_uncertainty_score":0.9984413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3684274790557637,"score_gpt":0.4809786938490395,"score_spread":0.1125512147932757,"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."}}