{"id":"W6976595728","doi":"10.6068/dp15dfe136cf566","title":"Trend 1990 - 2014. Food and Agriculture Organization of the United Nations. Food and Agriculture Organization Statistics: Emissions, Land Use - Total | Country: Canada | Item: Land Use total | Element: Net emissions/removals (CO2) - Gigagrams, 1990-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 067-001-041.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Land use; Greenhouse gas; Agricultural land; Land management; Land use, land-use change and forestry; Food security; Geospatial 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":[0.001323275,0.001930959,0.00184618,0.004950115,0.001166748,0.003131631,0.003308039,0.001299973,0.06248929],"category_scores_gemma":[0.009456573,0.001194588,0.00143666,0.02408022,0.0004609645,0.002852746,0.001656491,0.002739838,0.06119839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008215196,"about_ca_system_score_gemma":0.01714611,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7389116,"about_ca_topic_score_gemma":0.6714072,"domain_scores_codex":[0.9980877,0.0001537904,0.0002243956,0.0003763996,0.0007901243,0.0003675779],"domain_scores_gemma":[0.9910101,0.0005478822,0.0005691078,0.0004742208,0.007008165,0.0003905667],"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":[0.00001999671,0.000008103902,0.0007942652,0.0002582908,0.00002025156,0.000005661187,0.000008953844,0.0001017105,0.00001518842,0.0002559682,0.9972085,0.001303241],"study_design_scores_gemma":[0.000116837,0.00001010759,0.01297136,0.0005167485,0.00003759672,0.00001627614,0.0001660791,0.0002568503,0.000166607,0.000564797,0.9851392,0.00003753342],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003574071,0.00003068334,0.00001539809,0.00005209148,0.00002146777,0.000006303343,0.9993075,0.00003455695,0.0004963093],"genre_scores_gemma":[0.0002573526,0.00009775121,0.0001241938,0.00004574129,0.000008975124,0.00005417035,0.9984494,0.0000398278,0.0009226035],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2610884,"threshold_uncertainty_score":0.5252519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958203541134558,"score_gpt":0.2339591831364405,"score_spread":0.2143771477250949,"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."}}