{"id":"W2972910669","doi":"10.1016/j.coldregions.2019.102894","title":"The resilient moduli of five Canadian soils under wetting and freeze-thaw conditions and their estimation by using an artificial neural network model","year":2019,"lang":"en","type":"article","venue":"Cold Regions Science and Technology","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Soil water; Water content; Wetting; Cohesion (chemistry); Geotechnical engineering; Soil science; Plasticity; Subgrade; Moisture; Materials science; Environmental science; Geology; Chemistry; Composite material","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002392913,0.00007850143,0.00009863542,0.0001468562,0.001010707,0.00008328902,0.0001484956,0.00007689839,0.00001402111],"category_scores_gemma":[0.00002572915,0.00005514527,0.000007664611,0.0004673159,0.001365119,0.0002345538,0.00002803569,0.00009518495,0.000001268164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007076235,"about_ca_system_score_gemma":0.000131236,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0234348,"about_ca_topic_score_gemma":0.2681257,"domain_scores_codex":[0.9992475,0.00001658592,0.0001163484,0.0002314402,0.00008715281,0.0003010087],"domain_scores_gemma":[0.9995176,0.00007697171,0.00005955863,0.0001697662,0.0000667583,0.0001093837],"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":[0.0000377695,0.00005171712,0.4093571,0.00005381265,0.00003045519,0.000008035686,0.003106446,0.3399906,0.1455359,0.06155829,0.003274612,0.03699532],"study_design_scores_gemma":[0.00006210626,0.00006656957,0.006689189,0.00001570844,0.000005841655,0.00002579159,0.00256712,0.9782957,0.0004548898,0.01164674,0.00008681566,0.00008346631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961018,0.0005720461,0.00009403985,0.00254843,0.00007363176,0.0001706862,0.0003088311,0.00001491506,0.0001156236],"genre_scores_gemma":[0.9995715,0.0001102355,0.00009941308,0.0001446336,0.0000134875,0.000001371978,0.00003766426,0.000001856019,0.00001980775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6383052,"threshold_uncertainty_score":0.9830682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375101542517429,"score_gpt":0.2469891796968062,"score_spread":0.2132381642716319,"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."}}