{"id":"W2888077844","doi":"10.1016/j.ecolind.2018.08.041","title":"Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons","year":2018,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Abdus Salam International Centre for Theoretical Physics","keywords":"Trophic level; Plankton; Eutrophication; Phytoplankton; Zooplankton; Environmental science; Ecosystem; Biomass (ecology); Multivariate statistics; Lake ecosystem; Ecology; Oceanography; Nutrient; Computer science; Biology; Machine learning","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":[0.001272327,0.0006185725,0.0003062527,0.0009462436,0.0002828052,0.001219973,0.0004197225,0.0005304812,0.00101966],"category_scores_gemma":[0.004370944,0.0001997364,0.000321178,0.0007489952,0.0003095247,0.000723761,0.0005218583,0.000633534,0.000265094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008238731,"about_ca_system_score_gemma":0.0005931131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03789775,"about_ca_topic_score_gemma":0.03455427,"domain_scores_codex":[0.9998317,0.00005448742,0.00001322097,0.00004676428,0.00001304263,0.00004076508],"domain_scores_gemma":[0.9983301,0.00105449,0.0002088559,0.00005868879,0.0002012724,0.0001467032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004483903,0.0002086213,0.663922,0.00003115914,0.000120789,0.0001285421,0.0001004673,0.3034551,0.0009982404,0.0004526024,0.0009420277,0.02919223],"study_design_scores_gemma":[0.00001645161,0.00003386186,0.07834053,0.00001503687,0.00001518997,0.00001395593,0.00009999255,0.9201686,0.0002740893,0.000875307,0.0001356412,0.00001137306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966688,0.00008794798,0.002419666,0.0001137022,0.00001443154,0.000004232334,0.0003558255,0.00004752829,0.0002880094],"genre_scores_gemma":[0.9987326,0.00003296329,0.000544613,0.0000108615,0.00000520666,0.000002672795,0.0004572872,0.000002473044,0.0002113808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03789775,"threshold_uncertainty_score":0.07535434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007457480040957077,"score_gpt":0.2097439742037829,"score_spread":0.2022864941628258,"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."}}