{"id":"W4387138307","doi":"10.1111/poms.14083","title":"Extraction of visual information to predict crowdfunding success","year":2023,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Relevance (law); Data science; Artificial intelligence; Empirical research; Machine learning; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001390032,0.0006261211,0.0004637502,0.004508491,0.0003643153,0.001533626,0.0003086784,0.0006163156,0.003327107],"category_scores_gemma":[0.01234906,0.000163892,0.0005237587,0.001629508,0.000414186,0.00137979,0.0009242235,0.0005337457,0.001021617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004807283,"about_ca_system_score_gemma":0.0004861838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004390721,"about_ca_topic_score_gemma":0.004358863,"domain_scores_codex":[0.9993357,0.0001626366,0.000052364,0.0001297704,0.0002030549,0.0001164693],"domain_scores_gemma":[0.9943215,0.003133211,0.001123787,0.0002321807,0.0008854656,0.000303903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007942968,0.0005088726,0.7586111,0.000643147,0.0002073606,0.0004798906,0.001584972,0.01142314,0.006438509,0.001882598,0.009444475,0.2079817],"study_design_scores_gemma":[0.00003604214,0.0002840086,0.8392638,0.0003429632,0.000173512,0.0004973756,0.004102323,0.1362824,0.005730097,0.00372739,0.009469098,0.00009091754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708441,0.0008828864,0.01681997,0.0003354771,0.0000586871,0.0001935593,0.002815617,0.0003052486,0.007744392],"genre_scores_gemma":[0.9884627,0.0002781841,0.007930327,0.00004240116,0.00003902046,0.0001021184,0.002055862,0.00003081995,0.001058454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004508491,"threshold_uncertainty_score":0.01113027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148213667208251,"score_gpt":0.2650176464686128,"score_spread":0.2501962797477877,"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."}}