{"id":"W4402469225","doi":"10.14796/jwmm.h524","title":"Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study","year":2024,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Land cover; Artificial intelligence; Random forest; Cohen's kappa; Algorithm; Mathematics; Machine learning; Computer science; Classifier (UML); Boundary (topology); Pattern recognition (psychology); Statistics; Land use; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004593742,0.0001098141,0.0002054811,0.0002282451,0.00004853883,0.0002072247,0.00003622779,0.00002877435,0.00000611706],"category_scores_gemma":[0.00001899596,0.00008333618,0.00002181985,0.00006126886,0.00001773781,0.0002659672,0.00002821557,0.0002056761,0.000001716675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004081,"about_ca_system_score_gemma":0.000004201168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004799541,"about_ca_topic_score_gemma":0.00001166961,"domain_scores_codex":[0.999059,0.00006188641,0.000443397,0.000130457,0.0001908312,0.0001144953],"domain_scores_gemma":[0.9996548,0.00008314417,0.00005209645,0.00009231167,0.0000668913,0.00005070537],"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":[0.00003389934,0.00005669193,0.01619566,0.0004255685,0.0001886967,0.0007352704,0.001401819,0.9628152,0.001880168,0.000007509535,0.00005369833,0.01620578],"study_design_scores_gemma":[0.000508232,0.00007820601,0.0006926025,0.00007998328,0.0001918794,0.0001430807,0.0005643238,0.9971624,0.0002077612,0.00003963026,0.0002428168,0.00008908834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5603634,0.000127051,0.4393025,0.00002960972,0.00004816916,0.00009089654,0.000003056214,0.00002220803,0.00001314434],"genre_scores_gemma":[0.9901018,0.00004835621,0.009750938,0.000003312227,0.00003699969,8.912101e-7,0.00001312272,0.00002413713,0.00002044212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4297384,"threshold_uncertainty_score":0.339835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06950683480333616,"score_gpt":0.2973387997511235,"score_spread":0.2278319649477874,"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."}}