{"id":"W4246262303","doi":"10.4095/301047","title":"Labour Force Occupation, 2006 - Natural and Applied Sciences and Related Occupations (by census subdivision)","year":2010,"lang":"en","type":"report","venue":"","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Subdivision; Geography; Natural (archaeology); Natural science; Demographic economics; Demography; Economics; Sociology; Archaeology; Population; Physics","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":[0.00055566,0.0008013895,0.0003331717,0.003905407,0.001183373,0.0008073132,0.0007818409,0.0003523219,0.007770868],"category_scores_gemma":[0.001915716,0.0003752842,0.0004110817,0.00653276,0.0001593759,0.0005213674,0.0007278218,0.000745051,0.004626344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007935937,"about_ca_system_score_gemma":0.01481668,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9238483,"about_ca_topic_score_gemma":0.9617569,"domain_scores_codex":[0.9992034,0.00002616055,0.00006061473,0.0000617176,0.0004819228,0.0001661963],"domain_scores_gemma":[0.9982607,0.00003879954,0.0001281242,0.00003310738,0.001366235,0.000173055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001258024,0.0001370969,0.1309855,0.0007487822,0.00004935106,0.0001010216,0.0008746241,0.0007593874,0.0004224431,0.0008295828,0.8262888,0.03867759],"study_design_scores_gemma":[0.00002488621,0.0000404773,0.835034,0.0002313055,0.00002026206,0.00007199842,0.001038967,0.0004618495,0.0002774555,0.00008582391,0.1626974,0.0000155478],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03202742,0.0008581516,0.0001849129,0.0005195307,0.0001819203,0.0002561832,0.9421669,0.0001230562,0.02368202],"genre_scores_gemma":[0.0783056,0.004100548,0.001488568,0.0003041741,0.00007820215,0.000477592,0.8373011,0.00005057255,0.07789364],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07615173,"threshold_uncertainty_score":0.1532003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823648346449323,"score_gpt":0.3204651261613113,"score_spread":0.3022286426968181,"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."}}