{"id":"W4408484104","doi":"10.5194/egusphere-egu25-20681","title":"Improving Ice Segmentation in Permafrost Cores using Computed Tomography","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Permafrost; Thresholding; Segmentation; Artificial intelligence; Histogram; Image segmentation; Geology; Soil texture; Remote sensing; Ice core; Computer science; Pattern recognition (psychology); Soil science; Soil water; Image (mathematics); Climatology","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":[0.0006323188,0.0003982668,0.0003189003,0.00137922,0.0002712516,0.0007946267,0.0003744763,0.0004044476,0.0004652425],"category_scores_gemma":[0.001237337,0.0002363751,0.0002056604,0.000776958,0.0002717087,0.0004507893,0.0003396821,0.0001716971,0.0001941863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003336269,"about_ca_system_score_gemma":0.0003938497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004569034,"about_ca_topic_score_gemma":0.01337319,"domain_scores_codex":[0.9997863,0.00003795875,0.00001603939,0.00006535967,0.00006654157,0.00002775337],"domain_scores_gemma":[0.9995818,0.0001542664,0.00006450583,0.00003332216,0.0001467665,0.00001937005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003303245,0.0001018673,0.07157194,0.0002466543,0.00008328931,0.000430881,0.0005642373,0.0646603,0.6819539,0.0004097483,0.0003137226,0.1793331],"study_design_scores_gemma":[0.00002708122,0.0001480693,0.1953276,0.00004158737,0.00008436309,0.0003992247,0.0003430314,0.4954237,0.3056337,0.0005793119,0.001943677,0.00004862056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9076231,0.0003722403,0.09011506,0.00003861147,0.00001516582,0.00004904511,0.000131463,0.0004232514,0.001232121],"genre_scores_gemma":[0.8447255,0.000242641,0.1542698,0.00002413202,0.00001071889,0.00002388238,0.0002300941,0.00008234703,0.0003909386],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004569034,"threshold_uncertainty_score":0.00908488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04838423717305325,"score_gpt":0.2773482404331933,"score_spread":0.22896400326014,"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."}}