{"id":"W6957590067","doi":"10.6068/dp15df1eef73a4","title":"Trend 1996 - 2016. Bureau of Transportation Statistics. Border Crossings: Border Crossings - Trains | Country: USA | State: Texas, 1996-2016. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 007-003-006.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Train; Closing (real estate); Truck; Agency (philosophy); Visitor pattern; Statistical analysis; Track (disk drive); Descriptive statistics","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":[0.00145662,0.001718197,0.001398098,0.004594287,0.0009095473,0.002835544,0.002458351,0.001282449,0.06926437],"category_scores_gemma":[0.01340201,0.0009976437,0.001234761,0.01572457,0.0002986,0.003016944,0.001700856,0.002627042,0.100731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003034385,"about_ca_system_score_gemma":0.005468147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1673574,"about_ca_topic_score_gemma":0.120521,"domain_scores_codex":[0.9980752,0.0002127094,0.0003630994,0.0004131706,0.0006526382,0.0002831035],"domain_scores_gemma":[0.9916338,0.0007904028,0.0008886331,0.0007019799,0.005605788,0.0003794626],"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":[0.00002215374,0.000009578471,0.001160427,0.0001753613,0.00001222121,0.000005950903,0.00001449096,0.00009577945,0.00001747184,0.0003140562,0.9970552,0.00111733],"study_design_scores_gemma":[0.0001394902,0.00001664393,0.01417903,0.0005070028,0.00003061159,0.00002719283,0.0003169333,0.000298994,0.0001930839,0.0008414278,0.9834124,0.00003722856],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000691976,0.00002019269,0.00002448961,0.00005605204,0.00003744958,0.0000076511,0.9991911,0.00004920458,0.0005446191],"genre_scores_gemma":[0.0003464953,0.00005075503,0.0001332178,0.00003560497,0.00001442719,0.00007228757,0.9983101,0.00004739334,0.0009897619],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1673574,"threshold_uncertainty_score":0.3327667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03757357349607299,"score_gpt":0.3417409308482011,"score_spread":0.3041673573521281,"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."}}