{"id":"W4414860593","doi":"10.1016/j.engappai.2025.112579","title":"Predicting the compression index of expansive soils with hybrid machine learning approaches","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Expansive clay; Atterberg limits; Consolidation (business); Void ratio; Soil water; Compressibility; Mean squared error; Expansive","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001216095,0.001348717,0.0009585046,0.001987778,0.0002278742,0.00105251,0.001050351,0.001078056,0.0006335416],"category_scores_gemma":[0.00178746,0.000417273,0.00112147,0.001231522,0.0003113392,0.0008180654,0.000640472,0.0007477478,0.0002629521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005395879,"about_ca_system_score_gemma":0.0006539872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005911417,"about_ca_topic_score_gemma":0.00481183,"domain_scores_codex":[0.999634,0.00008589444,0.00003308985,0.0001188143,0.00007436834,0.0000537944],"domain_scores_gemma":[0.9990088,0.0005986636,0.0001210381,0.00004816142,0.0001900521,0.00003328268],"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":[0.0000917873,0.0001516045,0.005439179,0.00008853959,0.00009753464,0.00008398915,0.00003202055,0.909247,0.002117607,0.0003079735,0.0006221019,0.08172052],"study_design_scores_gemma":[0.000003034046,0.00002483024,0.0006435433,0.000006139448,0.000008926483,0.000006809367,0.000008077383,0.9984741,0.0004695305,0.0002495234,0.00009956963,0.000005975522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5451126,0.003116021,0.4444898,0.0004839149,0.0001273552,0.0001396187,0.001061429,0.0019329,0.003536439],"genre_scores_gemma":[0.9437473,0.0004254558,0.05335302,0.0001116263,0.00005388561,0.0001158334,0.0009847641,0.00002924153,0.001178869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005911417,"threshold_uncertainty_score":0.01175398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679797112450292,"score_gpt":0.2200688001720434,"score_spread":0.2032708290475405,"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."}}