{"id":"W6925204043","doi":"10.1594/pangaea.942397","title":"Arctic snow depth and sea ice thickness","year":2022,"lang":"en","type":"dataset","venue":"Publishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sea ice; Arctic ice pack; Arctic; Snow; Archipelago; Arctic dipole anomaly; Antarctic sea ice; Sea ice thickness","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003029754,0.0005063887,0.0005384258,0.0003440427,0.001823571,0.001922136,0.001282729,0.0003359842,0.000595607],"category_scores_gemma":[0.0003432884,0.000506571,0.0001052546,0.0003197199,0.000714433,0.001838623,0.002815875,0.001112018,0.000004865928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001946438,"about_ca_system_score_gemma":0.00007105384,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1914095,"about_ca_topic_score_gemma":0.1554392,"domain_scores_codex":[0.9962208,0.0001599185,0.0004885176,0.001216595,0.0007568357,0.001157331],"domain_scores_gemma":[0.9976817,0.000358837,0.0001085935,0.00128651,0.00003573677,0.0005285912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006453736,0.00008145419,0.0002806752,0.0006972974,0.0001973796,0.000008066425,0.00003524882,0.001320375,0.00001298408,0.0002061554,0.978543,0.01855276],"study_design_scores_gemma":[0.001125133,0.00009306118,0.0006835915,0.00006575198,0.0001953261,0.00006316608,0.0001308082,0.01585594,0.000002336798,0.0003594705,0.9808964,0.0005290662],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002801391,0.003711049,0.003125266,0.0003829276,0.00231788,0.001471301,0.9885318,0.00007903344,0.00010061],"genre_scores_gemma":[0.001252525,0.005669604,0.001065289,0.0001285428,0.0007152447,0.0002961638,0.9896526,0.00008736886,0.001132681],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03597026,"threshold_uncertainty_score":0.9997386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567198203002215,"score_gpt":0.2636857940184966,"score_spread":0.2180138119884744,"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."}}