{"id":"W6957771936","doi":"10.6068/dp14ba8865b1321","title":"Trend 2008 - 2009. Statistics Canada. CANSIM: Transportation - Transportation by Rail | Country: Canada | Table: Railway transport survey, balance sheet, by mainline companies | Variable: Stock-based employee compensation liabilities (non-current), Canadian National | Units: $CAD x 1,000, 2008-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-196.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Official statistics; Census; Compensation of employees; Summary statistics; Balance (ability); Statistical analysis; Social 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":[0.00220693,0.002215882,0.002521204,0.008230335,0.003284163,0.004718672,0.005058105,0.001410206,0.09758127],"category_scores_gemma":[0.01923517,0.001822805,0.001954641,0.03841022,0.0005901792,0.00267543,0.002277637,0.00305344,0.06070879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05288374,"about_ca_system_score_gemma":0.1374905,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946257,"about_ca_topic_score_gemma":0.9925566,"domain_scores_codex":[0.9957902,0.0002708162,0.0004534711,0.000533336,0.001950223,0.00100203],"domain_scores_gemma":[0.9643905,0.001193883,0.0009526256,0.001041164,0.03082166,0.001600216],"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":[0.00002150966,0.000005953868,0.0008989327,0.0002026606,0.00001574372,0.000006283844,0.00002005969,0.0000947967,0.000008819023,0.0003335097,0.9967637,0.001628046],"study_design_scores_gemma":[0.0001339801,0.00001228373,0.02331037,0.0008967641,0.00006477822,0.00002648007,0.0005055977,0.0004601455,0.0001704537,0.0006255275,0.9737107,0.00008290643],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005550077,0.00004575621,0.00002958455,0.0001260845,0.00003250442,0.00001623361,0.998575,0.0000654488,0.001053849],"genre_scores_gemma":[0.0008945618,0.0002981018,0.0004872669,0.0001657572,0.00001985478,0.0001449045,0.9926381,0.000141045,0.005210471],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09758127,"threshold_uncertainty_score":0.3837002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02635106883487737,"score_gpt":0.2478380263538787,"score_spread":0.2214869575190014,"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."}}