{"id":"W4386560828","doi":"10.32920/24085137","title":"Bylaws for biodiversity: re-modelling City of Toronto's Municipal Code Chapter 489: Grass and Weeds","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Environmental Conservation and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Toronto Metropolitan University; Centre for Social Innovation","funders":"","keywords":"Lawn; Biodiversity; Stewardship (theology); Environmental stewardship; Geography; Political science; Public administration; Law; Environmental resource management; Ecology; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003932923,0.0002038182,0.0002433418,0.00002284375,0.0001317547,0.00002642585,0.0002963898,0.0001377419,0.002285006],"category_scores_gemma":[0.000007852216,0.0002048138,0.0001064558,0.0000236642,0.0002056759,0.000123819,0.002347994,0.0001298228,0.00006097825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001774807,"about_ca_system_score_gemma":0.000002536011,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04272767,"about_ca_topic_score_gemma":0.02763132,"domain_scores_codex":[0.9987465,0.00002477692,0.0002651351,0.0004970416,0.0002615668,0.000205002],"domain_scores_gemma":[0.9993069,0.00005210076,0.000152364,0.0003946724,0.000004666299,0.00008930499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009239168,0.001472375,0.5559096,0.002392282,0.001671349,0.00003830416,0.01731756,0.2320918,0.005359727,0.02786762,0.1321239,0.02283152],"study_design_scores_gemma":[0.00333118,0.0005556761,0.1755537,0.0002600294,0.0007045094,0.000001773736,0.006673113,0.2972192,0.003019297,0.01464714,0.4953318,0.002702603],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8651738,0.0002044124,0.07209649,0.005094934,0.0006738889,0.002539098,0.0006102818,0.0002486721,0.05335843],"genre_scores_gemma":[0.9725252,0.001479357,0.01393703,0.00149362,0.00002343842,0.00007222089,0.0001666663,0.00002746869,0.01027498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3803559,"threshold_uncertainty_score":0.9986271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1089764457828767,"score_gpt":0.2739299386470816,"score_spread":0.1649534928642049,"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."}}