{"id":"W6976552103","doi":"10.6068/dp15df1f482a651","title":"Trend 1996 - 2016. Bureau of Transportation Statistics. Border Crossings: Border Crossings - Trucks | Country: USA, 1996-2016. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 007-003-005.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Closing (real estate); Descriptive statistics; Agency (philosophy); Vehicle miles of travel; Statistical analysis; Container (type theory)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001760152,0.00217354,0.002968352,0.0005368293,0.0007946567,0.00208114,0.006775098,0.001969604,0.03111687],"category_scores_gemma":[0.0001395542,0.002077254,0.000004638806,0.0001517383,0.003540168,0.001931158,0.0005120602,0.001792219,0.009418427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001652546,"about_ca_system_score_gemma":0.001514574,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8538509,"about_ca_topic_score_gemma":0.3172311,"domain_scores_codex":[0.9887605,0.0005624199,0.002596182,0.003624962,0.002494804,0.001961187],"domain_scores_gemma":[0.9828211,0.0009425536,0.004715702,0.0106113,0.00006521733,0.0008441489],"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":[0.0007783842,0.0004592623,0.000121273,0.001863898,0.0008292859,0.0006257754,0.00004069726,0.000006456518,0.0001258929,0.0007823879,0.9927818,0.001584892],"study_design_scores_gemma":[0.002952466,0.0002430667,0.0002082467,0.000402349,0.001745422,0.0001617461,0.00006427889,0.000808224,4.412052e-7,0.000003806958,0.9911129,0.002297061],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00000114307,0.004632473,0.0001026422,0.000003242,0.0011138,0.001453768,0.9739808,0.0003715742,0.01834058],"genre_scores_gemma":[0.00003076003,0.001547004,0.002662893,0.0001319317,0.0006692855,0.00005483038,0.9750335,0.001511456,0.01835838],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5366198,"threshold_uncertainty_score":0.9993261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03684022437439801,"score_gpt":0.3441501090859833,"score_spread":0.3073098847115853,"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."}}