{"id":"W4396770161","doi":"10.2139/ssrn.4821781","title":"Application of Interpretable Machine Learning and Causal Discovery to Understand Chlorophyll-A Variation in a Large Shallow Lake","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Variation (astronomy); Artificial intelligence; Computer science; Machine learning; Data science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001855892,0.0001757741,0.000275234,0.0001583751,0.00007522155,0.0001251716,0.0001844953,0.0001137226,0.00002415633],"category_scores_gemma":[0.00003505758,0.0001587161,0.00008695992,0.0002273355,0.00002827486,0.0001164403,0.000595699,0.002174772,0.00001651567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134534,"about_ca_system_score_gemma":0.0001494205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002126751,"about_ca_topic_score_gemma":0.01012601,"domain_scores_codex":[0.9980649,0.0001220613,0.0003677709,0.0003760426,0.0002972576,0.0007720143],"domain_scores_gemma":[0.9995888,0.00002754482,0.0001748847,0.0001395734,0.000008744797,0.00006038791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006764625,0.0009085081,0.6298673,0.0008573877,0.00182132,0.00003518116,0.03041449,0.1774933,0.09473938,0.03203429,0.00003473312,0.03111768],"study_design_scores_gemma":[0.001559192,0.0009022321,0.05289359,0.0009673431,0.0007242094,0.0001204753,0.005980875,0.1180115,0.001246574,0.8149115,0.001185176,0.001497401],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9078236,0.00081309,0.09049841,0.000425777,0.0001173486,0.0001482186,0.00002068124,0.00001735596,0.0001355226],"genre_scores_gemma":[0.9978735,0.0007120717,0.0001110676,0.000008185366,0.00009476652,0.000007931827,0.00001822622,0.00002063767,0.001153563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7828772,"threshold_uncertainty_score":0.9448424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006461531916248959,"score_gpt":0.2453220586478687,"score_spread":0.2388605267316197,"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."}}