{"id":"W4306966814","doi":"10.1016/j.polar.2022.100902","title":"Iceberg-seabed interaction analysis in sand by a random forest algorithm","year":2022,"lang":"en","type":"article","venue":"Polar Science","topic":"Offshore Engineering and Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Seabed; Random forest; Iceberg; Submarine pipeline; Subsea; Support vector machine; Geology; Arctic; Marine engineering; Environmental science; Computer science; Geotechnical engineering; Engineering; Machine learning; Sea ice; Oceanography","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":[0.0008467851,0.0007153733,0.001273427,0.001193032,0.0008414183,0.0007424197,0.001718651,0.001308588,0.002063781],"category_scores_gemma":[0.001768744,0.0006317024,0.001390578,0.001046732,0.0004638214,0.0009293341,0.0007895559,0.0008043947,0.0006234789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004995115,"about_ca_system_score_gemma":0.001640169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02641493,"about_ca_topic_score_gemma":0.02854802,"domain_scores_codex":[0.9996666,0.00008747037,0.00001650612,0.00008502039,0.00006676732,0.00007765026],"domain_scores_gemma":[0.9991058,0.0005832118,0.00004999815,0.00005714238,0.000150705,0.00005317521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001500064,0.00007229277,0.001715642,0.00003068381,0.00008910745,0.00007330555,0.0000199731,0.9510668,0.001082024,0.001329109,0.001192819,0.04317829],"study_design_scores_gemma":[0.000003703182,0.000003413093,0.00007334437,6.390782e-7,0.000003653865,0.000003483355,0.000002099762,0.9994947,0.00006506912,0.0002961904,0.00005237783,0.000001219263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05791397,0.0002035093,0.9394263,0.00008832088,0.00004681949,0.00005668212,0.000157441,0.0009821423,0.001124785],"genre_scores_gemma":[0.6176035,0.0001596702,0.3758046,0.0001149113,0.00009166632,0.000166759,0.001111343,0.0002225641,0.004724973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02641493,"threshold_uncertainty_score":0.05252236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004511553309979668,"score_gpt":0.2085616663027384,"score_spread":0.2040501129927587,"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."}}