{"id":"W154078727","doi":"10.4095/211127","title":"Ancient Pacific Margin NATMAP Project, year one","year":2000,"lang":"en","type":"report","venue":"","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Margin (machine learning); Oceanography; Geography; Paleontology; Geology; Archaeology; History; Computer science; Machine learning","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":[0.001031182,0.0006131729,0.0002067644,0.001797617,0.0007476438,0.001651295,0.0008374115,0.0003235528,0.02198169],"category_scores_gemma":[0.002108144,0.0003519451,0.0001504209,0.003583124,0.000185081,0.0008208237,0.001191161,0.0006016914,0.01295574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008712681,"about_ca_system_score_gemma":0.007010604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1420321,"about_ca_topic_score_gemma":0.1482555,"domain_scores_codex":[0.9997042,0.00003022626,0.00001810726,0.00004861669,0.0001531085,0.00004575132],"domain_scores_gemma":[0.9980412,0.00008880434,0.0001312089,0.0002610137,0.001032765,0.0004450723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001397717,0.00004670359,0.01679519,0.0002092992,0.00002052767,0.0001057928,0.0002174137,0.0003340178,0.0004247374,0.001700945,0.866455,0.1135506],"study_design_scores_gemma":[0.00004082867,0.00001639971,0.04358007,0.00008334896,0.00001111144,0.0001023042,0.0002030717,0.0004327984,0.0006084553,0.000550959,0.9543576,0.00001295396],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0238578,0.001200821,0.007355485,0.002377034,0.000533622,0.0008718836,0.7862292,0.003628697,0.1739454],"genre_scores_gemma":[0.01687853,0.001493641,0.02175033,0.0002767401,0.00009235115,0.0009667188,0.8206132,0.001062269,0.1368662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1420321,"threshold_uncertainty_score":0.2824109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0646246014073338,"score_gpt":0.2896083648402783,"score_spread":0.2249837634329445,"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."}}