{"id":"W2972206043","doi":"10.2139/ssrn.3432330","title":"Loser Takes All: Multiple Claimants & Probabilistic Restitution","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Legal principles and applications","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Restitution; Probabilistic logic; Unjust enrichment; Mathematics; Political science; Law; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011843,0.0008520436,0.00187036,0.002186269,0.006105587,0.009367693,0.004984639,0.01283752,0.07158147],"category_scores_gemma":[0.05003716,0.001192718,0.001435342,0.002619802,0.006905391,0.01143358,0.006762784,0.0108298,0.006266189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004317688,"about_ca_system_score_gemma":0.004555367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00944772,"about_ca_topic_score_gemma":0.009082192,"domain_scores_codex":[0.9905867,0.003792585,0.0003367625,0.001539181,0.002320542,0.001424284],"domain_scores_gemma":[0.9629677,0.02697368,0.003681168,0.003168343,0.002229609,0.000979488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004390445,0.00006427046,0.000892264,0.00005124719,0.00002721624,0.0002684784,0.0002836013,0.003080997,0.00004890647,0.9667805,0.01914408,0.009314302],"study_design_scores_gemma":[0.00003343097,0.00002413506,0.0009092903,0.00005279722,0.00004957485,0.0002910254,0.0003369187,0.02012123,0.0001675542,0.9629246,0.0150461,0.00004340844],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0848841,0.00474492,0.2511179,0.1019212,0.001338416,0.0003466018,0.001979957,0.001463479,0.5522034],"genre_scores_gemma":[0.8820252,0.001141095,0.007908684,0.003191817,0.0012141,0.0002278406,0.0001874283,0.0002507172,0.103853],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.07158147,"threshold_uncertainty_score":0.2394639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03329714907493829,"score_gpt":0.3206020554070611,"score_spread":0.2873049063321228,"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."}}