{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003282543,0.0001150515,0.000135433,0.0003357058,0.000125204,0.001159277,0.0005073874,0.00005288646,0.00002848271],"category_scores_gemma":[0.000119314,0.00009751572,0.00001582553,0.000381142,0.00003531955,0.006194387,0.0007667675,0.00008306238,0.0005589322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001404595,"about_ca_system_score_gemma":0.00002177512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005774404,"about_ca_topic_score_gemma":0.00001320091,"domain_scores_codex":[0.9991003,0.00000573624,0.0004033158,0.0001447172,0.0001995738,0.0001463981],"domain_scores_gemma":[0.9990512,0.00002196272,0.0001632075,0.000596897,0.000158185,0.000008509116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000932585,0.00007008349,0.06630728,0.00270492,0.00009663265,0.000003922316,0.0002583911,0.0001865313,0.00003771423,0.06206937,0.6112549,0.2569169],"study_design_scores_gemma":[0.0001738843,0.00000105441,0.001652158,0.0001882389,0.00001625485,0.000003332236,0.0003444723,0.04714496,0.00000787129,0.0001128576,0.9502535,0.0001013491],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02227759,0.001349201,0.6141291,0.004909383,0.01608783,0.001499986,0.0002291527,0.000611651,0.3389061],"genre_scores_gemma":[0.9958002,0.00003456548,0.00001855852,0.002505396,0.0005617705,0.00002000495,0.0005017993,0.000004510487,0.0005532161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9735226,"threshold_uncertainty_score":0.9998776,"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."}}