{"id":"W4398504278","doi":"10.7910/dvn/4qjw0v/wginpg","title":"Ballon d'Or Results - POP.tab","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"GNSS positioning and interference","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Computer graphics (images); Computer science; Geology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007569974,0.002172522,0.001215614,0.004625064,0.0007248282,0.00258051,0.001960504,0.001521631,0.08594064],"category_scores_gemma":[0.003718948,0.0005031147,0.001082421,0.00693623,0.0004449373,0.001153303,0.001581405,0.001593787,0.1385114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001462536,"about_ca_system_score_gemma":0.001958004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02854206,"about_ca_topic_score_gemma":0.05585666,"domain_scores_codex":[0.9990211,0.0001106623,0.0001089871,0.0002737572,0.0002973707,0.0001882026],"domain_scores_gemma":[0.9983062,0.0004685298,0.0001977881,0.0003647198,0.0004905731,0.0001722669],"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":[0.00004174443,0.00001404571,0.0006881731,0.0002990488,0.00001413455,0.00001459629,0.0000135255,0.0001772056,0.00007383682,0.0003880479,0.9966865,0.001589132],"study_design_scores_gemma":[0.0001188651,0.00001739029,0.005926731,0.0001940243,0.00001870948,0.0000469306,0.0000781957,0.000379805,0.0003001278,0.0007123076,0.992184,0.0000229024],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001279737,0.00005282357,0.00002629777,0.00003595702,0.0000298573,0.000004507039,0.9986474,0.0002304556,0.000844796],"genre_scores_gemma":[0.0003884711,0.00004137265,0.0001021208,0.00002576426,0.000007811085,0.00002020172,0.9984879,0.00005455968,0.0008717828],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9140593,"threshold_uncertainty_score":0.2875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215619591901497,"score_gpt":0.2358812540270723,"score_spread":0.2137250581080574,"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."}}