{"id":"W2901070953","doi":"","title":"Using a co-occurrence index to capture crop tolerance to climate variability: a case study of Peruvian farmers","year":2018,"lang":"es","type":"article","venue":"Americanae (AECID Library)","topic":"Agricultural and Food Production Studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Humanities; Geography; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001294772,0.0003235685,0.0002480087,0.001223522,0.0009583223,0.0009938234,0.0005052018,0.0007241205,0.00115275],"category_scores_gemma":[0.004175013,0.0001802467,0.0002949926,0.002278499,0.0004569016,0.0007515248,0.0007779381,0.0003089283,0.0001822444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007834511,"about_ca_system_score_gemma":0.000449556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02526401,"about_ca_topic_score_gemma":0.04154466,"domain_scores_codex":[0.9993615,0.0003829705,0.00002593841,0.00007918516,0.00006355635,0.00008691623],"domain_scores_gemma":[0.9978412,0.001117997,0.0004947986,0.0001259382,0.0003016568,0.0001183169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001461926,0.0002610245,0.9589908,0.0000754787,0.00007741238,0.003494268,0.01387235,0.001009981,0.002138787,0.0002860414,0.0003270381,0.0193206],"study_design_scores_gemma":[0.0000178266,0.00078868,0.9246939,0.00005066749,0.00009150754,0.00200879,0.05640241,0.01200595,0.0009259384,0.0003023263,0.00266849,0.00004338302],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989674,0.00006258947,0.0003796133,0.00005793346,7.525894e-7,0.00001506764,0.0000394351,0.000002702786,0.0004745103],"genre_scores_gemma":[0.9985234,0.0001095427,0.0008810475,0.00001517949,0.00000294067,0.0000260045,0.00006903876,0.000002438073,0.00037033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02526401,"threshold_uncertainty_score":0.0502339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03548594936655778,"score_gpt":0.2864858766517019,"score_spread":0.2509999272851441,"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."}}