{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004098628,0.0001572329,0.0002106903,0.0001841828,0.0001116861,0.0001507228,0.0005838363,0.0001330423,0.001659148],"category_scores_gemma":[0.0001101987,0.0001152497,0.0001800991,0.0002770258,0.00008804897,0.0004410749,0.0002819789,0.0006304266,0.00005687286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001161168,"about_ca_system_score_gemma":0.00002777962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004368139,"about_ca_topic_score_gemma":0.00000599103,"domain_scores_codex":[0.9982089,0.00006795777,0.0004215913,0.0001754259,0.0008930573,0.0002330906],"domain_scores_gemma":[0.9992443,0.00004877213,0.0004435393,0.00009837893,0.00005665797,0.0001083576],"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":[0.00008401768,0.00002365171,0.03433815,0.00001064661,0.0003685118,0.0002221272,0.001045719,0.9469612,0.006852183,0.00002960734,0.003863843,0.006200298],"study_design_scores_gemma":[0.002264341,0.000159102,0.05458323,0.0002909392,0.0002717886,0.004186909,0.0004901907,0.8709328,0.001131343,0.01462831,0.05041937,0.0006416918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803007,0.0002990785,0.01317633,0.0004285841,0.001657302,0.00009890653,0.00002646722,0.0000714745,0.003941147],"genre_scores_gemma":[0.9942487,0.00009036677,0.004620941,0.0001165886,0.0002657797,6.768104e-7,0.00002547769,0.00002348301,0.0006079467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07602845,"threshold_uncertainty_score":0.9992535,"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."}}