{"id":"W6976702473","doi":"10.6068/dp14ba7b9b68e24","title":"Trend 1994 - 2011. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Capital and repair expenditures, by sector and province | Variable: Aboriginal public administration (x 1,000,000), Capital and repair expenditures | Units: $CAD, 1994-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-034.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Capital (architecture); Descriptive statistics; Summary statistics; Public sector; Investment (military); Private sector","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001229057,0.0009178295,0.0009811053,0.0001282525,0.0004308482,0.0009689093,0.001068469,0.0004503017,0.005339357],"category_scores_gemma":[0.00008938831,0.0008392576,3.442981e-7,0.00006877728,0.0009526085,0.0008757878,0.0007491964,0.0006593819,0.000008887639],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002725823,"about_ca_system_score_gemma":0.007660155,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9983515,"about_ca_topic_score_gemma":0.9941362,"domain_scores_codex":[0.9939873,0.0006614414,0.000971749,0.002094317,0.001351533,0.000933674],"domain_scores_gemma":[0.9958774,0.0004972329,0.0009720328,0.001738434,0.00004031749,0.0008745471],"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":[0.0001359115,0.00004715284,0.0001470023,0.0007958732,0.00008209295,0.0004262606,0.000008319403,0.000003356855,0.0003119491,0.001357379,0.9966473,0.00003738727],"study_design_scores_gemma":[0.0007114208,0.0002429013,0.00002102316,0.00003763679,0.0001990756,0.001362652,0.0003002101,0.001891592,0.000001339642,0.000001572417,0.9942679,0.0009627148],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002420979,0.004609172,0.00001527867,0.000009157625,0.001605444,0.0007793557,0.9920716,0.0002106093,0.0004572558],"genre_scores_gemma":[0.0003973203,0.0007985458,0.001458296,0.0001435188,0.0003745616,0.00003520585,0.9953164,0.0001430513,0.001333068],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007387572,"threshold_uncertainty_score":0.9994058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01245402381717814,"score_gpt":0.2422596481446178,"score_spread":0.2298056243274396,"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."}}