{"id":"W6958713355","doi":"10.6084/m9.figshare.25587474.v2","title":"<b>Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques and GIS</b>","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Plant pathogens and resistance mechanisms","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Water quality; Principal component analysis; Sodium adsorption ratio; Alkalinity; Total dissolved solids; Irrigation; Pollution; Multivariate statistics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005559857,0.0001526449,0.0003090767,0.00002082576,0.00008473339,0.00006952514,0.0003255643,0.000141194,0.00921437],"category_scores_gemma":[0.00005552323,0.00004749106,0.00005934065,0.00008896849,0.00002162817,0.0001725455,0.000463372,0.0001248931,0.00001184871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007661429,"about_ca_system_score_gemma":0.00001367758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006316851,"about_ca_topic_score_gemma":0.0004511435,"domain_scores_codex":[0.9985412,0.0001483842,0.0004038167,0.0004045679,0.0002584993,0.0002435749],"domain_scores_gemma":[0.9994723,0.0001511949,0.000115497,0.0001469985,0.00005430824,0.00005970641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003077629,0.00008176036,0.001147551,0.001102984,0.00006763265,0.00004896931,0.001084874,2.604563e-7,0.979865,0.00006900322,0.001758101,0.01474314],"study_design_scores_gemma":[0.0004209359,0.0004152364,0.1551445,0.005054753,0.00008293067,0.00002757393,0.001726091,0.0001790776,0.6873791,0.0003707436,0.148275,0.0009240345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4777348,0.0003441306,0.000001932727,0.0001100159,0.00004515257,0.0002531113,0.5205892,0.00008228038,0.0008393499],"genre_scores_gemma":[0.9161217,0.00001371621,0.0003873775,0.00001890763,0.00007932723,0.00002860041,0.08320994,0.000001879077,0.0001385285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4383869,"threshold_uncertainty_score":0.9916914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1242485543126704,"score_gpt":0.3284559587789718,"score_spread":0.2042074044663013,"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."}}