{"id":"W7093075994","doi":"10.5194/tc-19-4785-2025","title":"Inferring inherent optical properties of sea ice using 360° camera radiance measurements","year":2025,"lang":"en","type":"article","venue":"The cryosphere","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada; Government of Canada; Polarforskningssekretariatet; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Networks of Centres of Excellence of Canada; Canada First Research Excellence Fund; ArcticNet","keywords":"Radiance; Radiative transfer; Sea ice; Terrain; Optical depth; Atmospheric radiative transfer codes; Inversion (geology); Irradiance; Scattering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002654674,0.0001469147,0.0001956928,0.00001840863,0.0002442135,0.00004508614,0.0002759424,0.00006336808,0.0003909763],"category_scores_gemma":[0.00003661833,0.00009801532,0.0000652019,0.0002315594,0.0002098786,0.0001404168,0.00003600398,0.0001968166,0.00003645147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002000089,"about_ca_system_score_gemma":0.0001877189,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007396935,"about_ca_topic_score_gemma":0.006378978,"domain_scores_codex":[0.9988521,0.00006804879,0.0002688025,0.0001976057,0.0003081951,0.000305235],"domain_scores_gemma":[0.999473,0.00005690532,0.00008315564,0.0002404637,0.00007957994,0.00006693311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000806673,0.00004172801,0.9199629,0.0001914105,0.0001176387,0.00000614713,0.0005387513,0.01647529,0.000560563,0.0001499597,0.0001596811,0.0617152],"study_design_scores_gemma":[0.001211763,0.000176118,0.6576251,0.00122431,0.0002908259,0.00004279285,0.003103357,0.3253773,0.001961833,0.001158943,0.007103678,0.0007239118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872037,0.001264272,0.00144802,0.0002476724,0.0003817881,0.0002404427,0.00001014486,0.00002669512,0.009177281],"genre_scores_gemma":[0.9976467,0.0000354477,0.001243696,0.0003188855,0.00005758223,0.000001541177,0.000004392631,0.000004414409,0.0006873131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.308902,"threshold_uncertainty_score":0.9992129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03985319644732186,"score_gpt":0.2350194487606717,"score_spread":0.1951662523133499,"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."}}