{"id":"W2939945347","doi":"10.3390/rs11080931","title":"Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Ministry of Forests","funders":"Universiti Teknologi Malaysia; Korea Institute of Geoscience and Mineral Resources","keywords":"Algorithm; Landslide; Computer science; Support vector machine; Machine learning; Mean squared error; Logistic model tree; Stochastic gradient descent; Artificial intelligence; Logistic regression; AdaBoost; Data mining; Statistics; Mathematics; Artificial neural network; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002355608,0.0001508186,0.0001520317,0.00004503742,0.0002119844,0.00004456235,0.00005321359,0.00008516949,0.0007178501],"category_scores_gemma":[0.00001864778,0.0001264761,0.00008270444,0.0001574004,0.00003751155,0.0001614997,0.0001098133,0.0003429572,0.0003874175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002051137,"about_ca_system_score_gemma":0.00001373025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006595885,"about_ca_topic_score_gemma":0.00001640201,"domain_scores_codex":[0.9987966,0.00003352942,0.0001925314,0.0003360217,0.0003474767,0.0002938505],"domain_scores_gemma":[0.9996291,0.00003034905,0.00008649859,0.0001548792,0.0000121908,0.00008691287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007207211,0.00004096296,0.01886611,0.00001813949,0.00008698137,0.00005697436,0.0002298314,0.3165977,0.2137431,0.000004307677,0.0001703952,0.4501134],"study_design_scores_gemma":[0.0005567019,0.00003836225,0.002706805,0.00005319525,0.00002758303,0.0005116249,0.00002484326,0.9801518,0.0006310804,0.00005454134,0.01508104,0.0001623973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7346839,0.00004934845,0.2576681,0.00007086653,0.0006437362,0.0001389134,0.00001041902,0.0001214475,0.006613293],"genre_scores_gemma":[0.9130636,0.00003210513,0.08336102,0.0001449535,0.0002548497,9.522899e-9,0.00007383474,0.00004384193,0.003025799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6635541,"threshold_uncertainty_score":0.7859954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0111476864204832,"score_gpt":0.200051802043222,"score_spread":0.1889041156227388,"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."}}