{"id":"W6939151611","doi":"10.6068/dp15df2207d8a94","title":"Trend 1996 - 2016. Bureau of Transportation Statistics. Border Crossings: Border Crossings - Trains | Country: USA | State: Maine, 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":[0.001365373,0.001723036,0.001420794,0.004653316,0.0008573295,0.002782191,0.002366027,0.00128599,0.06395411],"category_scores_gemma":[0.01298657,0.000996856,0.001216874,0.01611674,0.0002941806,0.003024871,0.001677116,0.002491378,0.09301548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003110345,"about_ca_system_score_gemma":0.005353568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1857525,"about_ca_topic_score_gemma":0.136142,"domain_scores_codex":[0.9981807,0.0001952055,0.0003427411,0.0003950709,0.0006188574,0.0002674481],"domain_scores_gemma":[0.99191,0.000736419,0.0008848733,0.0006518956,0.005463332,0.0003533787],"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":[0.00002283549,0.00000953443,0.001346057,0.0001902951,0.00001376154,0.000006308603,0.00001542068,0.0001021357,0.00001951932,0.00032214,0.9968067,0.001145222],"study_design_scores_gemma":[0.0001424446,0.00001722304,0.01701588,0.0005218085,0.00003319746,0.00002822449,0.0003128536,0.0003023915,0.0002030877,0.0007923992,0.9805933,0.00003725278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007455077,0.00002256983,0.00002293921,0.00005400633,0.00003508379,0.000007383293,0.9992003,0.00004735333,0.0005358143],"genre_scores_gemma":[0.0003725373,0.00005430152,0.0001237026,0.00003535989,0.00001373487,0.0000697216,0.9982343,0.00004539975,0.001050826],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1857525,"threshold_uncertainty_score":0.3693426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03555728192340084,"score_gpt":0.3400375298915367,"score_spread":0.3044802479681358,"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."}}