{"id":"W4390405734","doi":"10.5334/cstp.655","title":"Adopt a Lake: Successfully Tracking Harmful Cyanobacterial Blooms in Canadian Surface Waters Through Citizen Science","year":2023,"lang":"en","type":"article","venue":"Citizen Science Theory and Practice","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Norleaf Networks (Canada); Canadian Water Network; Université de Montréal","funders":"Université de Montréal; Génome Québec; Genome Canada","keywords":"Citizen science; Algal bloom; Bloom; Environmental science; Water quality; Tracking (education); Environmental planning; Environmental resource management; Ecology; Phytoplankton; Biology; Nutrient","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004018875,0.0004701958,0.0002969183,0.001611138,0.006062235,0.002404381,0.0009786139,0.0009257005,0.001952172],"category_scores_gemma":[0.005532669,0.0002612591,0.0003745797,0.002267736,0.001603771,0.001235165,0.003751012,0.001111563,0.0003774517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01765084,"about_ca_system_score_gemma":0.05224641,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9514909,"about_ca_topic_score_gemma":0.9812837,"domain_scores_codex":[0.9972518,0.0005039058,0.00006472354,0.0004333045,0.001216125,0.0005301409],"domain_scores_gemma":[0.9958256,0.0008102856,0.0002587975,0.0003290855,0.002089866,0.0006863343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005239533,0.0008024524,0.4261005,0.0008019165,0.00020666,0.001071245,0.06289999,0.00397657,0.04001676,0.005399081,0.0445331,0.4136677],"study_design_scores_gemma":[0.0001871227,0.0008598677,0.6146966,0.0004353759,0.0002146176,0.0002536856,0.08232085,0.02343265,0.01996195,0.004226605,0.2529423,0.0004684242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9176149,0.0006154802,0.01817667,0.008112485,0.0001359421,0.001777894,0.005788657,0.000903777,0.04687421],"genre_scores_gemma":[0.9269631,0.0009080583,0.05343434,0.001538482,0.00003146598,0.0008226373,0.002688228,0.0001006729,0.01351308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04850912,"threshold_uncertainty_score":0.1280664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557079697435051,"score_gpt":0.2883023301552585,"score_spread":0.272731533180908,"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."}}