{"id":"W6948927750","doi":"10.5281/zenodo.13578078","title":"Toronto Emergency Response - Open Data","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Plant chemical constituents analysis","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Upload; Python (programming language); Documentation; Christian ministry; Open data; Open source; Emergency response; Data collection","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.001284125,0.000230565,0.0002640122,0.00003280868,0.001353756,0.001768327,0.008606019,0.0001696672,0.1891401],"category_scores_gemma":[0.001102194,0.0001128918,0.00007347655,0.0005252819,0.00008888073,0.0004730187,0.0161738,0.0003697985,0.0424756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001786849,"about_ca_system_score_gemma":0.000003966486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287782,"about_ca_topic_score_gemma":0.0002057731,"domain_scores_codex":[0.9974174,0.0004716121,0.0003573454,0.0009387822,0.000449452,0.0003653393],"domain_scores_gemma":[0.9986848,0.00006139769,0.000137015,0.0006673927,0.0002237643,0.0002256343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001217953,0.00008338814,1.105064e-7,0.0000289151,0.00008314059,0.00003134453,0.00001207942,1.917683e-7,0.007589862,0.00001723724,0.9743738,0.01765814],"study_design_scores_gemma":[0.00006232266,0.00006908812,0.0000288506,0.00004583958,0.00008733557,0.00004189482,0.00007537248,0.0000141962,0.0000228395,0.00002723313,0.9992619,0.0002631717],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005010376,0.0003437285,0.000001283876,0.0009955988,0.0001729961,0.0003444044,0.9881524,0.0002730346,0.009215532],"genre_scores_gemma":[0.0005067778,0.0008347856,0.00001290854,0.00009094637,0.0003048778,4.562057e-8,0.9970362,0.00006887296,0.001144622],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1466645,"threshold_uncertainty_score":0.9999464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0878295788730224,"score_gpt":0.2979708763776751,"score_spread":0.2101412975046527,"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."}}