{"id":"W4385723698","doi":"10.5194/gmd-16-4521-2023","title":"Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms","year":2023,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Horizon 2020; Swiss Polar Institute; Deutsche Forschungsgemeinschaft; European Commission; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research; WSL-Institut für Schnee- und Lawinenforschung SLF","keywords":"Snow; Snowpack; Computer science; Machine learning; Artificial intelligence; Identification (biology); Artificial neural network; Segmentation; Algorithm; Data mining; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004600312,0.0001390708,0.0001755136,0.0001153104,0.0004133756,0.00006212643,0.0001958854,0.00003543559,0.0007048299],"category_scores_gemma":[0.00005871008,0.0001025084,0.00002687427,0.001173707,0.0000895577,0.0001070545,0.00003641937,0.00008776383,0.000304763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001293568,"about_ca_system_score_gemma":0.0001686716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002499695,"about_ca_topic_score_gemma":0.0007915666,"domain_scores_codex":[0.9985923,0.00002979733,0.0003134058,0.0003485873,0.0004041836,0.0003117685],"domain_scores_gemma":[0.9993909,0.00007385752,0.000137585,0.0001869327,0.0001415795,0.00006912512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000030934,0.00006509472,0.3098285,0.0001243399,0.0001050177,0.000006346819,0.003476133,0.08736213,0.0007490499,0.00004686153,0.004245223,0.5939603],"study_design_scores_gemma":[0.00009573951,0.00002754589,0.4298842,0.00003063308,0.000007300118,0.000001313347,0.0002033689,0.5650547,0.0002051061,0.00001971288,0.004366586,0.0001038021],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891273,0.0001198912,0.009319494,0.0001356465,0.0003784743,0.0002776871,0.00005595765,0.0001231909,0.0004623814],"genre_scores_gemma":[0.9148075,0.00006419799,0.07208837,0.0000347968,0.00001792408,0.00001429956,0.001440606,0.000009686085,0.01152264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5938565,"threshold_uncertainty_score":0.7717394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04671069915944705,"score_gpt":0.2312255547482457,"score_spread":0.1845148555887987,"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."}}