{"id":"W4398395157","doi":"10.7910/dvn/dnw5rw/lbcmlh","title":"map_element.xml","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"XML; Element (criminal law); Computer science; Information retrieval; World Wide Web; Political science","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":[0.0002512471,0.0002779731,0.0002800491,0.0002222211,0.0001345038,0.0004866865,0.001971698,0.0002105172,0.004157939],"category_scores_gemma":[0.0000532224,0.0002652188,0.00009029854,0.0002232585,0.0000576785,0.000930268,0.0008648389,0.0004022116,0.2536756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005910812,"about_ca_system_score_gemma":0.000316247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006043973,"about_ca_topic_score_gemma":0.000008490207,"domain_scores_codex":[0.9983048,0.00006483857,0.0002822467,0.0006780664,0.0003137965,0.0003562111],"domain_scores_gemma":[0.9975066,0.00003589234,0.0002459486,0.002037892,0.00008128343,0.00009241247],"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":[0.000002778237,0.00002721459,0.000001890378,0.0001261159,0.00002573083,0.00002213692,0.000007020142,0.000001770887,0.000006081057,0.00006000939,0.9851569,0.01456235],"study_design_scores_gemma":[0.0002614559,0.00002891968,0.000002809938,0.00008394405,0.00003154195,0.00008718464,0.000005782897,0.0007705148,0.00005143993,0.00009776573,0.9982566,0.0003220861],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[7.028617e-7,0.000003381969,0.02754803,0.00001261375,0.003349739,0.0001521702,0.9684676,0.0001028377,0.000362907],"genre_scores_gemma":[0.000001285503,0.0001462666,0.01303872,0.0003958899,0.0002272253,0.00001079148,0.9848921,0.00001064148,0.001277021],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2495177,"threshold_uncertainty_score":0.99998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539211705937298,"score_gpt":0.2409310968749253,"score_spread":0.2255389798155523,"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."}}