{"id":"W4381144906","doi":"10.11159/ijci.2023.002","title":"Identifying Soft Soils using Pore-Pressure Parameters: A Machine Learning Approach","year":2023,"lang":"en","type":"article","venue":"International Journal of Civil Infrastructure","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Soil water; Pore water pressure; Computer science; Machine learning; Soil science; Geotechnical engineering; Environmental science; Geology","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":[0.001758962,0.001450346,0.001447219,0.003010446,0.0004670813,0.001938357,0.001759856,0.002010021,0.001717325],"category_scores_gemma":[0.003205537,0.000505249,0.001471393,0.001701068,0.0005070046,0.001237528,0.0009350966,0.001957926,0.0009365224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005355398,"about_ca_system_score_gemma":0.0009710274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004225696,"about_ca_topic_score_gemma":0.002718813,"domain_scores_codex":[0.9992996,0.0001684322,0.00007959409,0.0002303687,0.0001220886,0.00009987868],"domain_scores_gemma":[0.9978973,0.00136899,0.0001787806,0.0001009311,0.0003673883,0.00008669946],"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":[0.0003257241,0.0009175403,0.02167849,0.0003468049,0.0003308838,0.0003462233,0.0001721499,0.407681,0.005151127,0.002046945,0.004143133,0.55686],"study_design_scores_gemma":[0.000009946245,0.00006845351,0.001753344,0.0000300862,0.00002619664,0.00003341115,0.00005130736,0.9945369,0.0006543134,0.002292194,0.0005271111,0.00001669743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1250295,0.002934819,0.8641279,0.0008711049,0.000144574,0.0002753945,0.001003714,0.002302126,0.003310893],"genre_scores_gemma":[0.7805562,0.001557271,0.2107622,0.0003842039,0.0002838518,0.0004672769,0.002664311,0.00009013718,0.003234609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004225696,"threshold_uncertainty_score":0.009302378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560417573866247,"score_gpt":0.2618507661993253,"score_spread":0.2462465904606628,"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."}}