{"id":"W6958008235","doi":"10.6068/dp15df3a9661414","title":"Trend 1961 - 2013. Food and Agriculture Organization of the United Nations. Food and Agriculture Organization Statistics: Production - Crops | Country: Canada | Item: Vegetables, fresh nes | Element: Area harvested - Ha, 1961-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 067-001-012.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Production (economics); Threshing; Yield (engineering); Crop; Scarcity; Agricultural productivity","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":[0.001800395,0.002017468,0.001929206,0.00587646,0.001223954,0.003276525,0.003673481,0.001250819,0.0739544],"category_scores_gemma":[0.01328956,0.001078203,0.001396716,0.02784261,0.0004923285,0.00255137,0.001624713,0.002688442,0.07461235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009162953,"about_ca_system_score_gemma":0.01987992,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7534723,"about_ca_topic_score_gemma":0.716987,"domain_scores_codex":[0.9977852,0.0001986207,0.0002532753,0.0004446557,0.0008985364,0.0004197832],"domain_scores_gemma":[0.9862408,0.001140928,0.0009899462,0.0009146775,0.0100176,0.0006961247],"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":[0.00002243163,0.000007671924,0.0007721747,0.0002400668,0.00001759657,0.000005297848,0.00001065644,0.0001138351,0.0000139824,0.0002710486,0.9973558,0.00116944],"study_design_scores_gemma":[0.0001048918,0.000009833705,0.0121221,0.0005040046,0.00002944135,0.00001537245,0.0001705618,0.0002212496,0.0001342551,0.0005645207,0.9860853,0.00003846899],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002989618,0.00002346408,0.00001585822,0.00003936189,0.0000155831,0.000005458172,0.9994453,0.00003523595,0.0003899262],"genre_scores_gemma":[0.0002065893,0.00006898828,0.0001198208,0.00003317688,0.000007458127,0.00004598639,0.998673,0.00003936397,0.0008057916],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2465277,"threshold_uncertainty_score":0.495959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357471869040903,"score_gpt":0.2455376762381337,"score_spread":0.2219629575477247,"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."}}