{"id":"W6957658958","doi":"10.6068/dp14ba7b4d77766","title":"Trend 1961 - 2003. Statistics Canada. CANSIM: Environment - Natural Resources | Country: Canada | Table: Timber assets | Variable: Timber assets (area), fire | Units: Hectares x 1,000, 1961-2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-086.","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; Natural resource; Census; Official statistics; Summary statistics; Hectare; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002068941,0.002166028,0.002285754,0.008591035,0.0031942,0.004831723,0.004794928,0.0013685,0.115214],"category_scores_gemma":[0.01621553,0.001748301,0.001810968,0.0443891,0.0006707969,0.002611523,0.002122701,0.002855643,0.06969421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05967672,"about_ca_system_score_gemma":0.1481503,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951311,"about_ca_topic_score_gemma":0.9927478,"domain_scores_codex":[0.9956442,0.0002377232,0.0004145096,0.0005355047,0.002188051,0.0009800131],"domain_scores_gemma":[0.9652384,0.001214375,0.0009759704,0.001066527,0.02994084,0.00156384],"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":[0.00001874324,0.000005737765,0.0008840848,0.0002078971,0.00001602817,0.000006417198,0.0000222566,0.0001215412,0.000009698204,0.0004146554,0.996581,0.001711978],"study_design_scores_gemma":[0.00009566462,0.00000798228,0.01842426,0.000562094,0.00004220613,0.00002030179,0.0003662839,0.0003617766,0.0001574773,0.0005917497,0.9793046,0.00006545235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004705729,0.00004330951,0.0000258756,0.0001142224,0.00002675744,0.00001445518,0.9983096,0.0000693926,0.001349438],"genre_scores_gemma":[0.000989768,0.0002959501,0.0004656102,0.0001680734,0.00001713962,0.0001325578,0.9906114,0.0001595108,0.007160005],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.115214,"threshold_uncertainty_score":0.432987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01839642891744199,"score_gpt":0.233500654561022,"score_spread":0.21510422564358,"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."}}