{"id":"W6957712376","doi":"10.6068/dp14ba8da65f346","title":"Trend 2001 - 2013. Statistics Canada. CANSIM: Construction - Nonresidential Engineering Construction | Country: Canada | Table: Public and private investment, summary by sector | Variable: Capital, machinery and equipment, Public, Agriculture, forestry, fishing and hunting (x 1,000,000) | Units: $CAD, 2001-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-036.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Agricultural Research and Practices","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Descriptive statistics; Census; Official statistics; Stock (firearms); Private sector; Public sector; Publication; Summary statistics; Statistical analysis","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006240387,0.0006311849,0.0006156992,0.00004687793,0.000407042,0.001563572,0.0008399123,0.000433697,0.001600596],"category_scores_gemma":[0.0003097148,0.0003359854,3.177435e-7,0.0003645343,0.0002820817,0.001396301,0.0008420963,0.0008411144,0.000002816175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001404763,"about_ca_system_score_gemma":0.0009974885,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9990891,"about_ca_topic_score_gemma":0.9979931,"domain_scores_codex":[0.9960884,0.0003483878,0.0005991465,0.001043138,0.0009047613,0.001016091],"domain_scores_gemma":[0.9970312,0.000845771,0.0006531451,0.0003656919,0.0001032184,0.001000965],"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":[0.00003109652,0.00002426863,0.0002871278,0.0002165058,0.0002008617,0.00007415882,8.303998e-7,0.000001479165,0.000233714,0.0003166953,0.9979261,0.0006872095],"study_design_scores_gemma":[0.0003639311,0.00008865017,0.00008651386,0.00003838431,0.0001395334,0.0004425613,0.0002941042,0.0007350057,1.601761e-7,0.000001027017,0.9971557,0.0006544004],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000112288,0.004783913,0.000001871503,0.00005859265,0.0004207343,0.000513193,0.9930237,0.00006801708,0.001017681],"genre_scores_gemma":[0.00005284944,0.00820522,0.0002897162,0.0001668175,0.0004309033,0.00001667343,0.9897488,0.00002211486,0.001066855],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.003421307,"threshold_uncertainty_score":0.9999092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02975690405977846,"score_gpt":0.2302823940903644,"score_spread":0.200525490030586,"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."}}