{"id":"W4280566752","doi":"10.3390/rs14102378","title":"Efficient Shallow Network for River Ice Segmentation","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Block (permutation group theory); Artificial intelligence; Remote sensing; Geology; Mathematics","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":[0.0001565305,0.0004928986,0.0002815693,0.0003752901,0.0002284564,0.0003885434,0.0005780342,0.0003405841,0.003135287],"category_scores_gemma":[0.0005027499,0.0002141344,0.0002739175,0.0003481534,0.0002509994,0.0008379634,0.0005621269,0.0003864056,0.0009354405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007884631,"about_ca_system_score_gemma":0.000760007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008406496,"about_ca_topic_score_gemma":0.0127964,"domain_scores_codex":[0.9999241,0.00000766507,0.000004129363,0.00002262363,0.000017615,0.00002387208],"domain_scores_gemma":[0.9998949,0.00002872061,0.00001397294,0.0000169639,0.00003508173,0.00001045639],"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":[0.0005276875,0.0001615119,0.004035987,0.0001626997,0.00008160573,0.0002079215,0.0001271889,0.4848006,0.07127906,0.007197994,0.01175889,0.4196589],"study_design_scores_gemma":[0.000006643359,0.00004945362,0.0006676402,0.000006893298,0.00001139745,0.00003283174,0.00001595831,0.9824005,0.01300714,0.001779443,0.002014802,0.000007262549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2552964,0.0006034368,0.7257625,0.0003175019,0.0001046797,0.0001099602,0.001119588,0.007369891,0.009315953],"genre_scores_gemma":[0.8101463,0.000264242,0.1757182,0.0001777749,0.00002794636,0.0001245524,0.002688393,0.0002201855,0.01063248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008406496,"threshold_uncertainty_score":0.01671511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274160953342524,"score_gpt":0.2158999543821573,"score_spread":0.2031583448487321,"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."}}