{"id":"W4401904680","doi":"10.2139/ssrn.4920293","title":"Lost-Premium Damages in M&A: Delaware's New Legal Landscape <br>","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Damages; Economics; Political science; Law","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":[0.001445997,0.000230425,0.0003628337,0.003149726,0.001347199,0.006577734,0.001423892,0.001964038,0.01372832],"category_scores_gemma":[0.005695747,0.000396502,0.000515376,0.002149486,0.001035252,0.002369341,0.001241432,0.002942592,0.001221142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01405784,"about_ca_system_score_gemma":0.005024653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.237141,"about_ca_topic_score_gemma":0.5193015,"domain_scores_codex":[0.9977856,0.0001947001,0.0001146591,0.0002621617,0.00112439,0.0005184859],"domain_scores_gemma":[0.9960986,0.0008513978,0.0006902336,0.0002054939,0.001467104,0.0006870988],"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":[0.000488721,0.0003561752,0.09359985,0.0002271776,0.000159321,0.001278222,0.001848201,0.01349546,0.001237862,0.3957851,0.3978247,0.09369916],"study_design_scores_gemma":[0.00006698455,0.0002060623,0.471396,0.0004081344,0.0001674207,0.0008257451,0.003195103,0.01915436,0.001367223,0.1045334,0.3984366,0.0002429674],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6002299,0.00814767,0.003740513,0.04713413,0.001114217,0.00008487998,0.02270859,0.0002585296,0.3165816],"genre_scores_gemma":[0.8856449,0.001071874,0.000571149,0.0018642,0.0002986811,0.00002972428,0.002426035,0.00006331603,0.1080301],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.237141,"threshold_uncertainty_score":0.4715213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02049260730984797,"score_gpt":0.2404929991527553,"score_spread":0.2200003918429073,"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."}}