{"id":"W6906387465","doi":"10.17632/85gy8ct4yn","title":"2019 Thames River (Canada) Cyanobacterial Bloom","year":2020,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tributary; Bloom; Water quality; Standing crop; Watershed; Hydrology (agriculture); Algal bloom; Sampling (signal processing)","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":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004826971,0.001034009,0.001178106,0.0001806304,0.0001883889,0.0002823527,0.008768791,0.0004994163,0.005726456],"category_scores_gemma":[0.0005783488,0.001036817,0.00009195322,0.0004431463,0.0002139189,0.0006498477,0.007168375,0.001202965,0.04075591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004436528,"about_ca_system_score_gemma":0.003680647,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5412254,"about_ca_topic_score_gemma":0.6854736,"domain_scores_codex":[0.9935964,0.0003605203,0.0007916528,0.002254775,0.001984915,0.001011702],"domain_scores_gemma":[0.9898595,0.0001461839,0.0006734676,0.008672894,0.00009728358,0.0005506926],"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":[0.0001871612,0.0000964264,0.000005755224,0.0001163991,0.0005228218,0.000505921,0.000007772735,9.147989e-7,0.0001716208,0.000003755924,0.9982012,0.0001802454],"study_design_scores_gemma":[0.0009826476,0.00005490187,0.00006663594,0.00009577591,0.0006544572,0.00003285777,0.00001279649,0.00002889851,0.0000725416,0.00001396831,0.9968685,0.001116018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002670276,0.0002080661,0.000002646437,0.0004218712,0.003181217,0.0006070856,0.9952366,0.0001746027,0.0001412114],"genre_scores_gemma":[0.00001027825,0.0004590573,0.0002269369,0.001015194,0.002869426,0.00002197189,0.994849,0.0002330783,0.0003151086],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1442482,"threshold_uncertainty_score":0.9992082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04639329699521273,"score_gpt":0.2664200736141225,"score_spread":0.2200267766189097,"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."}}