{"id":"W2962832465","doi":"","title":"Argus Controls: Aiming for the stars","year":2017,"lang":"en","type":"article","venue":"elib (German Aerospace Center)","topic":"Light effects on plants","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Argus; European union; Agriculture; Business; Political science; Geography; Engineering; International trade; Computer science; Archaeology","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":[0.001174228,0.0005581101,0.000283402,0.0004640166,0.001000677,0.001965938,0.0008181055,0.0010132,0.01726441],"category_scores_gemma":[0.001431437,0.0002521221,0.0002829627,0.0002771956,0.0009622896,0.002034578,0.001596092,0.001121651,0.006108799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009282247,"about_ca_system_score_gemma":0.001086745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003161618,"about_ca_topic_score_gemma":0.002767754,"domain_scores_codex":[0.9990948,0.00008518013,0.00003574701,0.0001789798,0.0004921469,0.0001131129],"domain_scores_gemma":[0.9991503,0.00009505486,0.00008305543,0.0001434505,0.0003259178,0.0002023627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002310405,0.0005367897,0.01254993,0.0005740452,0.00005865987,0.0008973205,0.002061224,0.004860304,0.2202735,0.08235712,0.1678602,0.5056605],"study_design_scores_gemma":[0.0003696052,0.001778815,0.009954518,0.0001899147,0.00005244271,0.0005571931,0.0006429096,0.01356663,0.09025449,0.01227643,0.8702746,0.00008249751],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2384568,0.001955645,0.3186911,0.0108636,0.00337097,0.001169742,0.001900795,0.04395818,0.3796332],"genre_scores_gemma":[0.6611012,0.0009379896,0.1194642,0.003645679,0.0007342206,0.0003972171,0.002412474,0.003548798,0.2077582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01726441,"threshold_uncertainty_score":0.05775523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02513094757739692,"score_gpt":0.2686362391296784,"score_spread":0.2435052915522815,"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."}}