{"id":"W4205783425","doi":"10.1002/ail2.62","title":"Deep learning does not replace Bayesian modeling: Comparing research use via citation counting","year":2022,"lang":"en","type":"article","venue":"Applied AI Letters","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Safety Canada","funders":"Columbia University","keywords":"Artificial intelligence; Deep learning; Computer science; Surprise; Machine learning; Citation; Dominance (genetics); Data science; Psychology; World Wide Web","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.06282485,0.0007455837,0.002019422,0.04503827,0.001519211,0.01166172,0.002373201,0.002599646,0.004689286],"category_scores_gemma":[0.4472364,0.0004039619,0.001697275,0.07516894,0.003053529,0.01789352,0.006173735,0.001862884,0.002038456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002374973,"about_ca_system_score_gemma":0.002899877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004674939,"about_ca_topic_score_gemma":0.003480603,"domain_scores_codex":[0.9259477,0.03823231,0.01159865,0.006755908,0.01556018,0.001905337],"domain_scores_gemma":[0.4289819,0.4485395,0.05642514,0.02757503,0.03541721,0.003061114],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001461996,0.0001810229,0.5016523,0.004452784,0.005690414,0.0004564083,0.008464146,0.01062046,0.0007871212,0.1399496,0.05062335,0.2756602],"study_design_scores_gemma":[0.0002949144,0.0006300756,0.3387042,0.009442138,0.005526755,0.001466997,0.01326816,0.08381549,0.005014572,0.3046781,0.2365436,0.0006149316],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6625174,0.07333449,0.08262508,0.05332046,0.003467918,0.0003519128,0.03381155,0.001932497,0.08863861],"genre_scores_gemma":[0.9721817,0.004635682,0.01087336,0.001613378,0.00112741,0.0002512413,0.006810889,0.0003665207,0.00213989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9549617,"threshold_uncertainty_score":0.3322536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07503285012804019,"score_gpt":0.3079581073745268,"score_spread":0.2329252572464867,"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."}}