{"id":"W2557988461","doi":"10.4043/27380-ms","title":"The Application of Automated SEM-Based Identification of Detrital and Diagenetic Mineral Phases in Offshore Cuttings from the Labrador Sea - Looking for the Source","year":2016,"lang":"en","type":"article","venue":"Arctic Technology Conference","topic":"Geological and Geochemical Analysis","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nalcor Energy (Canada); Memorial University of Newfoundland","funders":"University of Queensland","keywords":"Geology; Diagenesis; Heavy mineral; Sediment; Geochemistry; Submarine pipeline; Provenance; Source rock; Metamorphic rock; Igneous rock; Mineral; Mineralogy; Suite; Mineral resource classification; Paleontology; Oceanography; Archaeology; Structural basin","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":[0.000434776,0.0002435691,0.000127594,0.002278397,0.0003953769,0.0006825787,0.0003165407,0.0002377223,0.001600504],"category_scores_gemma":[0.0006620826,0.0001634142,0.0001681043,0.0007974686,0.0001700077,0.0002243475,0.0003169901,0.0001079176,0.000930422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003366914,"about_ca_system_score_gemma":0.0003706849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02584338,"about_ca_topic_score_gemma":0.1012012,"domain_scores_codex":[0.999805,0.00002749922,0.00002194358,0.00005797319,0.0000598429,0.00002780009],"domain_scores_gemma":[0.9993575,0.00009708039,0.00009150315,0.00005616375,0.0003658427,0.00003187048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003251007,0.0001069649,0.4636891,0.0001949452,0.00007626996,0.000245723,0.0006794989,0.002552337,0.3643812,0.0001995787,0.001093401,0.1664558],"study_design_scores_gemma":[0.000009004722,0.00006535534,0.9503754,0.00001958556,0.00002633081,0.0002080117,0.0003974364,0.01578367,0.03012973,0.00005850698,0.00291413,0.00001286898],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859856,0.0001674751,0.008027929,0.00003665745,0.000009825869,0.00008268552,0.002169429,0.000357685,0.003162723],"genre_scores_gemma":[0.9527302,0.0001125909,0.04244959,0.0000314383,0.000008069042,0.00003764308,0.002372052,0.00005945793,0.002198918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02584338,"threshold_uncertainty_score":0.05138594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008338781956122964,"score_gpt":0.2102118421436799,"score_spread":0.201873060187557,"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."}}