{"id":"W6943659306","doi":"10.17605/osf.io/dwtx6","title":"Bibliometric Analysis of the Machine Learning Applications in Fraud Detection on Crowdfunding Platforms","year":2024,"lang":"en","type":"article","venue":"Open Science Framework","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Publishing; Investment (military); Digital advertising; Artificial neural network; Equity (law); Random forest; Seed money; Bibliometrics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.00150018,0.0001414432,0.0002205034,0.05081692,0.0004930926,0.002495243,0.001837998,0.00007905923,0.00009172077],"category_scores_gemma":[0.001465184,0.00009918093,0.0001106582,0.4579045,0.000246195,0.002933666,0.0009319301,0.0005141819,0.00009913117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000139567,"about_ca_system_score_gemma":0.00004676938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007254294,"about_ca_topic_score_gemma":0.0002591825,"domain_scores_codex":[0.9982648,0.000004223058,0.0003118893,0.0005184482,0.000576262,0.0003243879],"domain_scores_gemma":[0.9989922,0.0002438047,0.0002059443,0.0004492579,0.00009527224,0.00001351301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001880765,0.0001091781,0.5420452,0.0001123715,0.00006353213,0.000002990291,0.0001358855,0.01065982,0.002056784,0.2841066,0.00005016515,0.1606387],"study_design_scores_gemma":[0.0001046567,0.00002676899,0.7617119,0.0006042441,0.0001481788,8.148913e-7,0.0002340942,0.1746335,0.003113444,0.03543183,0.02360987,0.0003806165],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9455889,0.0001368556,0.03041783,0.0001978076,0.0004306571,0.0005325709,0.000004330645,0.00008510322,0.02260591],"genre_scores_gemma":[0.9991111,0.000009591288,0.0002804751,0.0001543191,0.00007667975,0.00005720116,0.000004006927,0.00001518386,0.0002914902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4070876,"threshold_uncertainty_score":0.9985403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0279173345542434,"score_gpt":0.3062840258799778,"score_spread":0.2783666913257344,"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."}}