{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01247129,0.0008143201,0.0007872866,0.002423752,0.0004436672,0.001668466,0.0009373208,0.0009886011,0.0007590287],"category_scores_gemma":[0.01933933,0.0002374299,0.001395801,0.002095012,0.0008793113,0.00156462,0.0007509953,0.0006918965,0.0002907166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059477,"about_ca_system_score_gemma":0.0006990913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004429028,"about_ca_topic_score_gemma":0.004022694,"domain_scores_codex":[0.9933561,0.003264907,0.00056982,0.001019426,0.001511199,0.000278516],"domain_scores_gemma":[0.9778206,0.01526952,0.001431521,0.001319374,0.003913573,0.0002453008],"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":[0.00185597,0.001480625,0.4668433,0.0008014363,0.001509338,0.001198072,0.001220476,0.2099425,0.006039511,0.005417135,0.004527208,0.2991645],"study_design_scores_gemma":[0.00007287646,0.002085908,0.1612976,0.0001003996,0.0003313391,0.0008064015,0.001822999,0.8181073,0.006772697,0.004095241,0.004396929,0.0001102698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9526696,0.0008369546,0.04341397,0.0002229559,0.00006618675,0.0002064073,0.0006203044,0.0002242557,0.001739353],"genre_scores_gemma":[0.9697207,0.0002033161,0.02861515,0.00003313924,0.00004127098,0.0001310037,0.0006661068,0.00004587447,0.0005432616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01247129,"threshold_uncertainty_score":0.06595528,"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."}}