{"id":"W4379508482","doi":"10.1007/s11248-023-00354-w","title":"GEnZ explorer: a tool for visualizing agroclimate to inform research and regulatory risk assessment","year":2023,"lang":"en","type":"article","venue":"Transgenic Research","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McGill University","funders":"Foreign Agricultural Service","keywords":"Transparency (behavior); Risk assessment; Biology; Visualization; Data science; Risk analysis (engineering); Computer science; Business; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01335282,0.0002040332,0.0002870915,0.0003032778,0.001576411,0.0004820866,0.0008779261,0.0002064875,0.0003665018],"category_scores_gemma":[0.0008060125,0.00009736413,0.0001001926,0.004253658,0.0003858703,0.0001901497,0.0005582939,0.0008811858,0.0005521547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001636427,"about_ca_system_score_gemma":0.0001283276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001875875,"about_ca_topic_score_gemma":0.0002964156,"domain_scores_codex":[0.9934417,0.0006524422,0.000443858,0.0008611311,0.002555728,0.002045152],"domain_scores_gemma":[0.9950144,0.00286904,0.00002683923,0.0002834119,0.001235879,0.0005704357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003214928,0.0001298028,0.003065497,0.0001220866,0.00005137774,0.000008321019,0.001746459,0.00002423916,0.8565339,0.005024614,0.006023535,0.1269486],"study_design_scores_gemma":[0.0009869247,0.004493934,0.8537758,0.00008046535,0.00001197304,0.000005777712,0.005310981,0.001163697,0.06923393,0.01364254,0.05070367,0.0005903146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902593,0.00009222108,0.00006711421,0.006332395,0.00004873547,0.002352917,0.0001100982,0.0001551588,0.0005820052],"genre_scores_gemma":[0.9957214,0.0008260558,0.0009111719,0.00004038089,0.0002206921,0.0009905358,0.00006688943,0.000009783782,0.001213073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8507103,"threshold_uncertainty_score":0.9997234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3244624424621861,"score_gpt":0.4701047490184415,"score_spread":0.1456423065562554,"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."}}