{"id":"W3208197580","doi":"10.5281/zenodo.4082121","title":"Mehrabi et al. 2020. The global divide in data-driven farming. Supplementary Information.","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Agricultural Development and Management","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Agriculture; Computer science; Geography; Data science; Archaeology","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":[0.003014565,0.0014303,0.0008446476,0.002753962,0.0007367216,0.002938192,0.001879331,0.001457346,0.04313176],"category_scores_gemma":[0.01279526,0.0006214266,0.001210006,0.005190942,0.0003950033,0.001852531,0.002384661,0.001790141,0.04000665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00173959,"about_ca_system_score_gemma":0.002831982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04361445,"about_ca_topic_score_gemma":0.07001351,"domain_scores_codex":[0.9987957,0.0003393044,0.0001514965,0.0002209565,0.0003441222,0.0001484649],"domain_scores_gemma":[0.9965335,0.001061125,0.0004119601,0.0007026388,0.0008442363,0.000446407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0000568962,0.00001280937,0.001653944,0.0005058629,0.00004732654,0.00001238789,0.00001985247,0.0002299528,0.00003844408,0.000872408,0.9930118,0.003538299],"study_design_scores_gemma":[0.0002296546,0.00001978896,0.01074341,0.0006679108,0.00007166706,0.0000670586,0.0001299049,0.0005496808,0.0002142953,0.003157459,0.9841114,0.00003789516],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001976015,0.000183325,0.0001164379,0.0003184126,0.00007214455,0.00001415573,0.9978507,0.0002097026,0.001037509],"genre_scores_gemma":[0.001136543,0.0001612374,0.0006502896,0.0001952008,0.00001930874,0.00008298573,0.9969997,0.00009299262,0.0006617291],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04361445,"threshold_uncertainty_score":0.1442901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03934515196174695,"score_gpt":0.2457008041188368,"score_spread":0.2063556521570899,"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."}}