{"id":"W4398557349","doi":"10.7910/dvn/dnw5rw/vio63a","title":"map_question_validation.xml","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; XML; Information retrieval; Database; World Wide Web","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001789652,0.0002305451,0.0002172882,0.0001673917,0.0001191952,0.0003611892,0.001830604,0.0001289826,0.003698922],"category_scores_gemma":[0.00003854242,0.0002448094,0.0001023871,0.0003549835,0.00003106812,0.0005298323,0.0007783315,0.0002629974,0.3738444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000582729,"about_ca_system_score_gemma":0.0002340718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005946989,"about_ca_topic_score_gemma":0.000005794162,"domain_scores_codex":[0.9984128,0.00006278787,0.0002862767,0.0005993645,0.0004365145,0.0002022898],"domain_scores_gemma":[0.9970931,0.0001437893,0.0002323545,0.002263963,0.0001584323,0.0001083571],"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":[0.000001160991,0.00005335805,4.203102e-7,0.0000271206,0.00002033478,0.000004034338,0.00000563158,0.0002177099,0.000003146316,0.02979076,0.9692748,0.0006015174],"study_design_scores_gemma":[0.0001449788,0.00001531115,0.00002905814,0.00002254086,0.00001938604,0.000007843676,0.00000116359,0.002873093,0.000005818367,0.004235139,0.9923691,0.000276577],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[4.600356e-7,5.754722e-7,0.1316904,0.00005777836,0.0006883226,0.0002412571,0.8668532,0.00008109338,0.0003868868],"genre_scores_gemma":[0.00001041675,0.00004835745,0.007082363,0.0008158241,0.0002261691,0.00004210204,0.9911397,0.00001038146,0.0006246943],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3701454,"threshold_uncertainty_score":0.9983037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257288027554626,"score_gpt":0.2740371604337164,"score_spread":0.2483083576782538,"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."}}