{"id":"W4249684988","doi":"10.1017/wsc.2016.30","title":"Another view","year":2017,"lang":"en","type":"article","venue":"Weed Science","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Herbicide resistance; Weed; Resistance (ecology); Weed control; Weed science; Profit (economics); Agroforestry; Business; Economics; Biology; Agronomy; Microeconomics","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":[0.0002691264,0.00005047036,0.00006273744,0.000004923899,0.001508499,0.000297939,0.0009722768,0.00001969146,0.0004002313],"category_scores_gemma":[0.00005993387,0.00001652609,0.00002843264,0.0001241798,0.0003940338,0.0002664822,0.000100417,0.00004067805,0.0003145307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000963695,"about_ca_system_score_gemma":0.00001150612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005048926,"about_ca_topic_score_gemma":0.0007486233,"domain_scores_codex":[0.9993435,0.000005582504,0.00006892263,0.0002092234,0.0001716419,0.0002011929],"domain_scores_gemma":[0.9996489,0.00002356773,0.000062659,0.0001501983,0.00005236416,0.00006226011],"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":[7.869263e-7,0.00000803174,0.0001873893,2.261225e-7,4.136011e-7,2.317301e-7,0.000007078514,1.039951e-7,0.8658664,0.00436479,0.0001486794,0.1294159],"study_design_scores_gemma":[0.00003870269,0.00002019335,0.8378419,0.000003293847,0.000002038724,0.000001234239,0.00001533278,0.00005446441,0.003114552,0.00213272,0.1567087,0.00006685365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8951598,0.0001034837,0.00001893948,0.01137004,0.0001139492,0.000196475,0.000005819719,0.0000658171,0.09296562],"genre_scores_gemma":[0.9978688,8.065298e-7,0.00009644062,0.0005903599,0.0001271592,0.00002678422,5.905605e-7,2.128445e-7,0.001288836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8627518,"threshold_uncertainty_score":0.9997914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0324608687805276,"score_gpt":0.2615882296306573,"score_spread":0.2291273608501296,"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."}}