{"id":"W4399765478","doi":"10.32920/26052487","title":"Clustering and Characterization of Toronto Soils Using Pressuremeter Tests","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Soil water; Geotechnical engineering; Characterization (materials science); Environmental science; Geography; Geology; Soil science; Mathematics; Statistics; Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002930276,0.0004593381,0.0002638026,0.003579029,0.0005799416,0.001122816,0.0004950459,0.0003159733,0.001116967],"category_scores_gemma":[0.001161921,0.0001473441,0.000262494,0.004168292,0.0004370225,0.0003286287,0.0004398832,0.0001627918,0.0003872581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003012763,"about_ca_system_score_gemma":0.001740903,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3900162,"about_ca_topic_score_gemma":0.5406083,"domain_scores_codex":[0.9995319,0.00003084792,0.00002477764,0.00009743441,0.0002313477,0.00008371015],"domain_scores_gemma":[0.9993441,0.00007420561,0.0001225938,0.00004374921,0.000351653,0.00006371309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004751851,0.0001356137,0.7011797,0.0002696102,0.0001187605,0.0006581949,0.001844538,0.06363415,0.07475708,0.001062725,0.003179348,0.1526851],"study_design_scores_gemma":[0.00001122293,0.00006582343,0.8580322,0.00002170807,0.00003690378,0.0001061046,0.0012651,0.1171582,0.01975774,0.0002316923,0.003272509,0.00004085398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860761,0.0001141312,0.008068637,0.00003436726,0.000006547245,0.00006495585,0.00237206,0.0002387573,0.003024429],"genre_scores_gemma":[0.9905055,0.00006893232,0.006107427,0.000007040618,0.000004802035,0.00002310137,0.002128419,0.00001957826,0.001135091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6099838,"threshold_uncertainty_score":0.7754921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01984851890097911,"score_gpt":0.252078344769357,"score_spread":0.2322298258683779,"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."}}