{"id":"W6901669357","doi":"10.6068/dp15df2239e5275","title":"Trend 1996 - 2016. Bureau of Transportation Statistics. Border Crossings: Border Crossings - Trucks | Country: USA | State: California, 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; CONQUEST","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":[0.001971886,0.002328223,0.003115756,0.0005646038,0.0008373036,0.002158976,0.006452868,0.001757951,0.02601032],"category_scores_gemma":[0.0001454477,0.002205916,0.000005206098,0.0001721175,0.003647173,0.001721791,0.0005437588,0.001921783,0.0107982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002082295,"about_ca_system_score_gemma":0.001817094,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8771652,"about_ca_topic_score_gemma":0.3760815,"domain_scores_codex":[0.9877939,0.0006410339,0.00292514,0.003726998,0.002700279,0.002212607],"domain_scores_gemma":[0.9828977,0.0009737837,0.005032923,0.01002767,0.00007895062,0.000988974],"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":[0.001009449,0.0004870081,0.000160205,0.002107886,0.0008951065,0.0007450172,0.00004705111,0.00001426448,0.0001086475,0.0002664577,0.9923206,0.001838305],"study_design_scores_gemma":[0.003114725,0.0002470507,0.0001398037,0.0003794996,0.001596527,0.0001805045,0.00006667055,0.001116384,5.988201e-7,0.000005195076,0.9906915,0.002461506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00000196257,0.00420649,0.0001729317,0.000003542021,0.0009651064,0.00164115,0.9840793,0.000424377,0.008505138],"genre_scores_gemma":[0.00002888287,0.001823568,0.002387567,0.0001392442,0.0005151101,0.00006345229,0.979168,0.001615931,0.01425825],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5010836,"threshold_uncertainty_score":0.999538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03141532811060182,"score_gpt":0.3344795797799266,"score_spread":0.3030642516693248,"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."}}