{"id":"W4409344517","doi":"10.1080/17517575.2025.2490920","title":"Big data and Omnipresent AI","year":2025,"lang":"en","type":"article","venue":"Enterprise Information Systems","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Big data; Computer science; Data science; Data mining","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":[0.009700627,0.0004920709,0.0006838082,0.002201251,0.001744375,0.008405268,0.001506857,0.002217897,0.006364918],"category_scores_gemma":[0.01978698,0.0004175854,0.0003891996,0.003262003,0.008486226,0.01657982,0.004680114,0.005394644,0.00163559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001690134,"about_ca_system_score_gemma":0.001916455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605213,"about_ca_topic_score_gemma":0.002068498,"domain_scores_codex":[0.9946477,0.001849834,0.0001933752,0.0006115804,0.002451594,0.0002459537],"domain_scores_gemma":[0.9793763,0.01422602,0.0007324301,0.002571705,0.002050448,0.001043107],"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":[0.0000733882,0.00003213531,0.001670422,0.0004798998,0.00005766098,0.000147763,0.0008667259,0.001016088,0.0004367688,0.832853,0.09442537,0.06794081],"study_design_scores_gemma":[0.00001592596,0.00001670998,0.0008570159,0.0003508606,0.00001399979,0.0001906781,0.0007417449,0.002244436,0.0003131829,0.7072055,0.2880231,0.00002683325],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01713886,0.1248229,0.09835218,0.5226641,0.009604879,0.0001045688,0.002296372,0.0009435415,0.2240726],"genre_scores_gemma":[0.5808892,0.1411856,0.09712346,0.1047915,0.02548491,0.0005100062,0.003202351,0.0006872826,0.04612571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009700627,"threshold_uncertainty_score":0.05130243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0627055246316916,"score_gpt":0.2954843611064689,"score_spread":0.2327788364747773,"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."}}