{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001288049,0.0003606551,0.000359607,0.000153344,0.00005145111,0.0001521307,0.0007178805,0.0003357622,0.006204482],"category_scores_gemma":[0.0001325338,0.0003284079,0.00008510287,0.0001130994,0.0000395966,0.0002392437,0.0001785912,0.0005693849,0.2570608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026888,"about_ca_system_score_gemma":0.00004756701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002029216,"about_ca_topic_score_gemma":0.0001145717,"domain_scores_codex":[0.9985759,0.00003177506,0.0003702044,0.0004047932,0.0002543555,0.0003629891],"domain_scores_gemma":[0.9982187,0.00007070171,0.00007238694,0.001487893,0.00003610589,0.0001142432],"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":[0.00008324772,0.00002329678,1.97258e-7,0.0002166511,0.00006809972,0.00009920992,0.00001804048,0.0004821427,0.00003223588,0.000006223004,0.998881,0.00008964389],"study_design_scores_gemma":[0.0004815843,0.00007738297,0.00001440967,0.0003864724,0.0000756367,0.00002838196,0.00001492109,0.0005604791,0.00006726511,0.000003378265,0.9978738,0.0004162815],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001710193,0.000001801389,0.00005837943,0.000001858608,0.002400417,0.0001651497,0.9924335,0.0002031938,0.004718564],"genre_scores_gemma":[0.0001857541,0.0004079661,0.0002155232,0.0001049796,0.0003843671,0.00001526973,0.9958695,0.00004001404,0.002776692],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2508563,"threshold_uncertainty_score":0.9999168,"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."}}