{"id":"W3033687227","doi":"","title":"IDENTIFYING LIABILITY OF FOREIGNNESS USING EBAY AUCTION RESULTS","year":2005,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Liability; Business; Commerce; Advertising; Internet privacy; Computer science; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01293142,0.0004139794,0.0009491542,0.004143415,0.0007221458,0.002911616,0.000830682,0.001023863,0.004339964],"category_scores_gemma":[0.06634637,0.0002559447,0.000654327,0.003103775,0.001256135,0.002899738,0.001567872,0.001303557,0.00069299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092964,"about_ca_system_score_gemma":0.00113652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01152227,"about_ca_topic_score_gemma":0.008016122,"domain_scores_codex":[0.9945573,0.002601856,0.0005676876,0.0005254131,0.00122243,0.0005252932],"domain_scores_gemma":[0.8861353,0.05626682,0.04294927,0.006428034,0.006569485,0.001651016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004012341,0.0001772442,0.9413578,0.0001191785,0.0002972815,0.0009993629,0.0006343469,0.00871795,0.0003645726,0.0208876,0.001412048,0.02463136],"study_design_scores_gemma":[0.0001579499,0.0009855886,0.7076904,0.0003532455,0.0008777701,0.003998271,0.005695895,0.1757627,0.007074231,0.08560962,0.01156059,0.00023367],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751974,0.0008218796,0.01243983,0.0005692958,0.00002137237,0.00007602698,0.000731021,0.00003384311,0.01010934],"genre_scores_gemma":[0.9971284,0.0002013665,0.001039539,0.00004109904,0.00002478746,0.00001027413,0.0003037262,0.000003775499,0.00124696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01293142,"threshold_uncertainty_score":0.0683887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6582465723840153,"score_gpt":0.6728497770388048,"score_spread":0.01460320465478948,"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."}}