{"id":"W4392925717","doi":"10.1007/s12665-024-11488-3","title":"Monitoring cyanobacterial blooms: a strategy combining predictive modeling and remote sensing approaches","year":2024,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Comisión Sectorial de Investigación Científica; Agencia Nacional de Investigación e Innovación","keywords":"Algal bloom; Environmental science; Warning system; Remote sensing; Bloom; Phytoplankton; Environmental resource management; Computer science; Oceanography; Ecology; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001731347,0.001011668,0.0007066918,0.00181527,0.0004865981,0.001319305,0.0009287432,0.0006489062,0.0004140111],"category_scores_gemma":[0.002041626,0.0003685225,0.0006381657,0.001119363,0.0003047829,0.002033375,0.00082313,0.0004040206,0.0001015166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035522,"about_ca_system_score_gemma":0.00143123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01709225,"about_ca_topic_score_gemma":0.02411393,"domain_scores_codex":[0.9995329,0.0001441396,0.00002770794,0.0001173041,0.0001496492,0.00002821592],"domain_scores_gemma":[0.9991232,0.0003415311,0.0001947374,0.0001223741,0.000171914,0.00004633618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002789183,0.0009148676,0.09939735,0.0002799352,0.0005485537,0.00006864026,0.0001002319,0.5703154,0.05171017,0.002599235,0.001644331,0.2721423],"study_design_scores_gemma":[0.00003003046,0.0001017805,0.014404,0.00002351081,0.0001188091,0.00002029158,0.00006344759,0.9770588,0.005512798,0.001925888,0.0007137962,0.00002678652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5297711,0.001587084,0.4540392,0.00169611,0.0001011961,0.0003676817,0.001294861,0.002365403,0.008777489],"genre_scores_gemma":[0.9065176,0.0005843025,0.09154467,0.0001775523,0.0000431613,0.0001160806,0.0005693673,0.00003477985,0.0004125513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01709225,"threshold_uncertainty_score":0.03398556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03083404561700563,"score_gpt":0.1977252064699997,"score_spread":0.1668911608529941,"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."}}