{"id":"W4390984159","doi":"10.1002/nem.2261","title":"<scp>Deeper</scp>: A shared liquidity decentralized exchange design for low trading volume tokens to enhance average liquidity","year":2024,"lang":"en","type":"article","venue":"International Journal of Network Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Market liquidity; Security token; Computer science; Market maker; Liquidity crisis; Business; Liquidity risk; Monetary economics; Accounting liquidity; Liquidity premium; Token ring; Computer security; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.001313433,0.0001983056,0.0002517047,0.0003304563,0.0001334761,0.000418606,0.002008618,0.0000949947,0.0000386189],"category_scores_gemma":[0.00007632635,0.0001934856,0.0002150027,0.0004591241,0.00003270724,0.0004118237,0.000432085,0.0002578983,0.00002999657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002717184,"about_ca_system_score_gemma":0.00005120536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003135096,"about_ca_topic_score_gemma":0.000003888795,"domain_scores_codex":[0.9979814,0.00007972104,0.000578525,0.0004145318,0.000524829,0.0004209715],"domain_scores_gemma":[0.9986489,0.0003037711,0.0002311029,0.0003377741,0.0003264231,0.0001520097],"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":[0.000122784,0.000449529,0.000046225,0.0001684916,0.001296247,0.0006225868,0.002373145,0.05679591,0.0003382623,0.09599383,0.7293347,0.1124584],"study_design_scores_gemma":[0.0004433076,0.0002990396,0.0001948543,0.0004034007,0.00004847336,0.00008267266,0.0000330839,0.4385546,0.001321308,0.03500787,0.5234559,0.0001554632],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01583945,0.001246575,0.9741495,0.005414811,0.002344952,0.0006154214,0.00001399352,0.0001612107,0.000214057],"genre_scores_gemma":[0.7746166,0.0009867265,0.221022,0.001180554,0.00103072,0.0001987654,0.000007978681,0.00002544303,0.0009312173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7587771,"threshold_uncertainty_score":0.7890114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189282179407002,"score_gpt":0.285516949589406,"score_spread":0.2665887316487058,"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."}}