{"id":"W2014990401","doi":"10.1016/j.quageo.2006.04.002","title":"Deciphering the Holocene evolution of the St. Lawrence River drainage system using luminescence and radiocarbon dating","year":2006,"lang":"en","type":"article","venue":"Quaternary Geochronology","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Geology; Holocene; Radiocarbon dating; Deglaciation; Pleistocene; Thermoluminescence dating; Chronology; Drainage basin; Period (music); Geochronology; Paleontology; Authigenic; Physical geography; Sediment; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004077636,0.0001200025,0.0001321955,0.001240241,0.0005905016,0.001102047,0.0003432918,0.0002568595,0.0009784017],"category_scores_gemma":[0.001131518,0.0001831909,0.00008296278,0.001433508,0.0005351617,0.0006529681,0.0004695707,0.0003539388,0.0002244322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001776347,"about_ca_system_score_gemma":0.002538662,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3286406,"about_ca_topic_score_gemma":0.7186686,"domain_scores_codex":[0.9998342,0.00003123152,0.00001537134,0.00004356162,0.00003259582,0.00004307781],"domain_scores_gemma":[0.9994227,0.00007494343,0.0001377813,0.00003888733,0.0002754428,0.00005029151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007286086,0.00003526969,0.9246536,0.00007854485,0.00004691621,0.000123412,0.002877259,0.0006057239,0.01111768,0.001254141,0.0006021912,0.05853245],"study_design_scores_gemma":[0.000002101056,0.00001296022,0.9911364,0.00002503883,0.00001305672,0.0000513442,0.001026981,0.000732428,0.0007805759,0.0001128538,0.0060995,0.000006688252],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961212,0.0004432768,0.0004501282,0.0001531998,0.000004360444,0.000003693785,0.000274068,0.00001522848,0.002534781],"genre_scores_gemma":[0.9974735,0.0003042341,0.0006504365,0.00003998934,0.000004229091,0.000004472228,0.0003283208,0.000009429627,0.001185233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6713594,"threshold_uncertainty_score":0.6534555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290739585499556,"score_gpt":0.2172252924636635,"score_spread":0.2043178966086679,"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."}}