{"id":"W7008390931","doi":"","title":"Boosting farmer income: further insights from great cases","year":2019,"lang":"en","type":"other","venue":"Socio-Environmental Systems Modeling","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"MaRS","funders":"","keywords":"Boosting (machine learning); Agriculture; Work (physics); Data collection","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001672839,0.0003234914,0.0003518992,0.0005070294,0.0006402413,0.001656224,0.0008653999,0.0006581916,0.01818296],"category_scores_gemma":[0.005821041,0.0001385889,0.0003778851,0.0009510742,0.0007874981,0.001476228,0.001103672,0.0009845119,0.000778425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001365756,"about_ca_system_score_gemma":0.000748775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01317447,"about_ca_topic_score_gemma":0.02496276,"domain_scores_codex":[0.9995402,0.0002793749,0.000006481032,0.00003578847,0.00006187173,0.00007637939],"domain_scores_gemma":[0.9980949,0.001343065,0.00009823479,0.0001677841,0.0001681265,0.0001278415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002066096,0.0005791431,0.02615988,0.0001819326,0.000097195,0.0006289234,0.001101177,0.2166373,0.0006503146,0.6142473,0.04824382,0.09126651],"study_design_scores_gemma":[0.00008011433,0.0001058387,0.01572453,0.000113723,0.00006423992,0.0001443262,0.002906559,0.509779,0.0009615457,0.4122135,0.05786418,0.00004246594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.510789,0.0008734227,0.05124579,0.008766325,0.000119635,0.00008860436,0.00158699,0.0002535807,0.4262767],"genre_scores_gemma":[0.9739377,0.0002997433,0.005564732,0.0001705189,0.00002145108,0.00003662635,0.0002711969,0.00006876945,0.01962927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01818296,"threshold_uncertainty_score":0.06082809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045064218576732,"score_gpt":0.2244570790798616,"score_spread":0.2040064368940943,"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."}}