{"id":"W6920422068","doi":"10.6068/dp14ba8eed66c12","title":"Trend 2000 - 2006. Statistics Canada. CANSIM: Information and Communications Technology - Business and Government Internet Use | Country: Canada | Table: Survey of electronic commerce and technology, barriers to electronic commerce, by North American Industry Classification System (NAICS) | Variable: Users/non-users of Internet who do not use electronic commerce, Transportation equipment manufacturing, Security concerns | Units: %, 2000-2006. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-125.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"The Internet; Business statistics; Government (linguistics); Official statistics; Economic statistics; Census; Information and Communications Technology; Information technology; Summary 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":[0.002023986,0.002448019,0.002701428,0.00986637,0.003642451,0.004682442,0.005277698,0.001507372,0.06667817],"category_scores_gemma":[0.01820706,0.00163748,0.002195976,0.04517686,0.0006225036,0.002502225,0.00242316,0.003249994,0.04209258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05026087,"about_ca_system_score_gemma":0.1427808,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9942738,"about_ca_topic_score_gemma":0.9929312,"domain_scores_codex":[0.9956017,0.0002515001,0.0004779256,0.0005118058,0.002170257,0.0009868013],"domain_scores_gemma":[0.9606053,0.001205619,0.001219375,0.0008843874,0.03441262,0.001672663],"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":[0.00002218095,0.000008213295,0.001364307,0.0002607715,0.00002079441,0.000006399644,0.00002350429,0.00009783624,0.000007646307,0.0003301892,0.9963736,0.001484622],"study_design_scores_gemma":[0.0001703263,0.00001787499,0.0414788,0.001048575,0.00009435293,0.00003152805,0.0007716296,0.0005802599,0.0002088699,0.0006349513,0.9548637,0.00009918399],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006872049,0.00004760171,0.00002014493,0.000116627,0.00002871814,0.00001535117,0.998974,0.00004350841,0.0006854224],"genre_scores_gemma":[0.0008155808,0.0002540434,0.0002741458,0.0001347439,0.00001957023,0.0001117201,0.9947537,0.00006491617,0.003571597],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06667817,"threshold_uncertainty_score":0.3646699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01825023926837992,"score_gpt":0.2407908717419111,"score_spread":0.2225406324735312,"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."}}