{"id":"W2737873585","doi":"10.3390/s17071658","title":"Surface Estimation for Microwave Imaging","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Microwave imaging; Imaging phantom; Computer science; Microwave; Computer vision; Laser; Set (abstract data type); Noise (video); Artificial intelligence; Optics; Image (mathematics); Physics; Telecommunications","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":[0.0005332217,0.0007528372,0.0005728263,0.0008123465,0.0002238388,0.0008888491,0.0007084007,0.00102205,0.004038649],"category_scores_gemma":[0.003458566,0.0003182449,0.0006594527,0.001022032,0.0005865965,0.0009016551,0.0009237432,0.001339923,0.00250066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004314388,"about_ca_system_score_gemma":0.0003535158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001114253,"about_ca_topic_score_gemma":0.000938902,"domain_scores_codex":[0.9995673,0.0001316029,0.00001905511,0.00008159716,0.0001750625,0.00002540207],"domain_scores_gemma":[0.9992338,0.0003903668,0.000067933,0.0001313031,0.0001574061,0.00001925081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001028843,0.00004593822,0.0008580502,0.0003939702,0.00007763797,0.0001110015,0.0001241477,0.2685368,0.03910287,0.09619705,0.01099826,0.5834514],"study_design_scores_gemma":[0.000006511189,0.00002494292,0.0004385632,0.00002774923,0.00001111699,0.0001503726,0.00002806062,0.9390382,0.006468919,0.03854726,0.01523974,0.00001844725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00100693,0.0004447181,0.9971243,0.000106233,0.00003546513,0.00001393377,0.00004460895,0.0002963803,0.0009274252],"genre_scores_gemma":[0.1304207,0.002780249,0.8580567,0.000228915,0.0002824694,0.0002112089,0.0007665036,0.0004296824,0.006823512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004038649,"threshold_uncertainty_score":0.01351064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243666965872461,"score_gpt":0.2494885921977467,"score_spread":0.2370519225390221,"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."}}