{"id":"W4388306959","doi":"10.1007/978-3-031-40447-4_9","title":"Inland Water Quality Monitoring Using Remote Sensing and GIS Techniques—A Tigris River, Iraq Case Study","year":2023,"lang":"en","type":"book-chapter","venue":"Springer proceedings in earth and environmental sciences","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Water quality; Mean squared error; Remote sensing; Environmental science; Turbidity; Coefficient of determination; Hydrology (agriculture); Statistics; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001641321,0.0004066879,0.0003802369,0.0001754719,0.0006156865,0.0001860201,0.0001677516,0.0002061237,0.00008829436],"category_scores_gemma":[0.000008869718,0.0003344951,0.00005123658,0.00008498636,0.001232725,0.0004223845,0.0008830323,0.0004109605,0.00004328212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001787316,"about_ca_system_score_gemma":0.000008750276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005252259,"about_ca_topic_score_gemma":0.0001829094,"domain_scores_codex":[0.997327,0.0000339109,0.0005041329,0.0009660498,0.0006434033,0.0005255058],"domain_scores_gemma":[0.9994533,0.00003048151,0.0001793875,0.0001354362,0.000002945548,0.0001984667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007406603,0.0001734447,0.7067913,0.000241898,0.00009206525,0.001573758,0.03291012,0.00009324015,0.02819413,0.0001654115,0.00004135183,0.2296492],"study_design_scores_gemma":[0.005220791,0.003796556,0.7435092,0.002977287,0.0006957331,0.007386673,0.07551645,0.007328951,0.0489399,0.02563306,0.067485,0.01151038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855092,0.00005490943,0.00002026653,0.00006673154,0.0001497473,0.0005296024,0.00001186457,0.00006679195,0.01359095],"genre_scores_gemma":[0.9846438,0.0003033342,0.004865823,0.00003338417,0.0001031986,0.000001997575,0.000001907888,0.00003407805,0.01001246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2181388,"threshold_uncertainty_score":0.9999107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.064207700045275,"score_gpt":0.2963911433999498,"score_spread":0.2321834433546748,"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."}}