{"id":"W4223496291","doi":"10.5430/jms.v13n1p13","title":"Critical Assessment of Issues and Benefits of Digital Asset Management","year":2022,"lang":"en","type":"article","venue":"Journal of Management and Strategy","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital asset management; Agile software development; Asset (computer security); Productivity; Business; Asset management; Focus (optics); Distribution management system; Management system; Computer science; Process management; Risk analysis (engineering); Operations management; Computer security; Finance; Economics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005456212,0.0001523865,0.0003259693,0.0004178512,0.0001207787,0.0002361305,0.0003040808,0.00002508504,0.0002790705],"category_scores_gemma":[0.00001541868,0.0001332402,0.00006839381,0.0003227265,0.0001123922,0.00120872,0.0006145231,0.0001487836,0.000001091275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001416657,"about_ca_system_score_gemma":0.000008603994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001816788,"about_ca_topic_score_gemma":0.00000201717,"domain_scores_codex":[0.998431,0.00001331756,0.0006010503,0.0001769918,0.0005977848,0.0001798516],"domain_scores_gemma":[0.9991515,0.00003843706,0.0004502609,0.0001558559,0.000184649,0.00001929149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002109934,0.000799393,0.07200758,0.002894491,0.0004704701,0.0001549768,0.00003891792,0.001313462,0.00005089968,0.7769684,0.009411002,0.1356794],"study_design_scores_gemma":[0.002851478,0.0007985702,0.788426,0.0006266756,0.001327673,0.000127984,0.009227669,0.003907378,0.0001062836,0.05613514,0.1355825,0.000882622],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9153502,0.00337294,0.001862765,0.001779226,0.0006868105,0.0004133677,0.00006554247,0.00002219549,0.07644694],"genre_scores_gemma":[0.9980953,0.0006398789,0.0006292183,0.00007757421,0.0001417468,0.000004652903,0.00001386041,0.00001114985,0.0003866323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7208333,"threshold_uncertainty_score":0.5433377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06464473405767256,"score_gpt":0.3274290189390244,"score_spread":0.2627842848813519,"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."}}