{"id":"W6901703543","doi":"10.6068/dp15e79b2631693","title":"Trend 1990 - 2015. Food and Agriculture Organization of the United Nations. Food and Agriculture Organization Statistics: Emissions, Land Use - Forest Land | Country: Canada | Item: Net Forest conversion | Element: Implied emission factor for CO2 - tonnes CO2/ha, 1990-2015. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 067-001-043.","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; Food security; Stock (firearms); Agricultural land; European union; Natural resource; Forest inventory","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":[0.001277698,0.001671883,0.0014785,0.004004812,0.0007296156,0.002414746,0.002563235,0.001050253,0.05801787],"category_scores_gemma":[0.008411665,0.001002737,0.001136673,0.01790649,0.0003297187,0.002621384,0.001517411,0.002204532,0.06498672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003905211,"about_ca_system_score_gemma":0.007439259,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3404236,"about_ca_topic_score_gemma":0.2478416,"domain_scores_codex":[0.9984288,0.0001586357,0.0002267729,0.0003422973,0.0005801252,0.0002633769],"domain_scores_gemma":[0.9938351,0.0005449041,0.0005852065,0.0004828368,0.004271916,0.0002800302],"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":[0.00002290377,0.000009558373,0.001000055,0.0002571515,0.00001900366,0.000006619091,0.00001041287,0.0001224294,0.00002164465,0.0003445845,0.9963048,0.001880904],"study_design_scores_gemma":[0.0001069637,0.00001282624,0.01295116,0.0004865035,0.000029183,0.00001905192,0.000131778,0.000251426,0.0001704482,0.0007385116,0.9850721,0.00002997339],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004289654,0.00002636089,0.00002276263,0.00003756544,0.0000211379,0.000006402018,0.9993493,0.0000338221,0.0004597953],"genre_scores_gemma":[0.0002687872,0.00007339401,0.0001452021,0.00003297579,0.000007955725,0.00006005643,0.9985586,0.00003503001,0.0008180786],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6595764,"threshold_uncertainty_score":0.6768843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01825726831616902,"score_gpt":0.2413326471972916,"score_spread":0.2230753788811226,"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."}}