{"id":"W4281388723","doi":"10.5194/tc-16-1963-2022","title":"A probabilistic seabed–ice keel interaction model","year":2022,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement; Université du Québec à Rimouski; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Keel; Sea ice; Seabed; Geology; Bathymetry; Arctic ice pack; Drift ice; Antarctic sea ice; Lead (geology); Arctic; Fast ice; Sea ice thickness; Climatology; Oceanography; Geomorphology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005708626,0.0007474397,0.001026502,0.0007062518,0.0006104719,0.001347709,0.002267571,0.001630414,0.00490643],"category_scores_gemma":[0.001380994,0.0007926732,0.0009333169,0.001089021,0.00110542,0.001439211,0.001594202,0.0009999474,0.0004572048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009053939,"about_ca_system_score_gemma":0.001194426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0146267,"about_ca_topic_score_gemma":0.009259195,"domain_scores_codex":[0.9995857,0.0001230998,0.00002161318,0.0001059976,0.00009056453,0.00007313863],"domain_scores_gemma":[0.9994005,0.0002744866,0.0001122855,0.00003824133,0.00009920447,0.00007528115],"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":[0.00001114634,0.000006840178,0.0003786434,0.000006293498,0.000009566403,0.0000238719,0.000007406435,0.9961029,0.0001834092,0.002591913,0.00006130154,0.0006168084],"study_design_scores_gemma":[0.000004383085,0.000005377838,0.0001269577,0.000001232709,0.000003132184,0.000005806522,0.000002954049,0.9985233,0.00003280299,0.001173668,0.0001173504,0.000003007186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2731546,0.0004728074,0.7032362,0.0007175313,0.000104262,0.0001049089,0.001404306,0.0004749273,0.02033043],"genre_scores_gemma":[0.9748747,0.0001939622,0.01607302,0.00008402886,0.00004157406,0.0001477256,0.0004853444,0.00006242529,0.008037199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0146267,"threshold_uncertainty_score":0.02908307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0139002119323662,"score_gpt":0.2103795138199485,"score_spread":0.1964793018875823,"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."}}