{"id":"W4387861103","doi":"10.61186/jgit.11.1.19","title":"Forest Classification Using Simulated Compact Polarimetry Data and Deep Learning Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Geospatial Information Technology","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Polarimetry; Computer science; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Geology; Optics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004392965,0.0007651972,0.000341737,0.0008620113,0.0002803449,0.0005380607,0.0005084412,0.0004113161,0.0006019759],"category_scores_gemma":[0.0009251949,0.0001814569,0.0003680977,0.0009509338,0.0003046319,0.0008125292,0.0003568805,0.0005289647,0.0001792434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000225,"about_ca_system_score_gemma":0.0006603128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03992312,"about_ca_topic_score_gemma":0.04792921,"domain_scores_codex":[0.9998401,0.00003083848,0.000007672954,0.00004084532,0.00004227416,0.00003820795],"domain_scores_gemma":[0.9997019,0.000113977,0.00003598133,0.0000291443,0.0001028436,0.00001615129],"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":[0.0003931035,0.000241114,0.01280011,0.00007543845,0.00007466075,0.0001332456,0.0000698733,0.7426125,0.009074911,0.0008410412,0.001434129,0.2322499],"study_design_scores_gemma":[0.000003956904,0.00001345939,0.001029919,0.000002719159,0.000005318191,0.000004770443,0.00001602627,0.997017,0.001518171,0.0002744335,0.0001113598,0.000002921101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8680735,0.0007875783,0.1249527,0.0003468464,0.00006771014,0.00006340146,0.0006998019,0.001444883,0.003563656],"genre_scores_gemma":[0.9629192,0.0001861643,0.03408348,0.00004284667,0.00001614924,0.00002496701,0.001345,0.00002235209,0.001359855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03992312,"threshold_uncertainty_score":0.07938153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02234483050068553,"score_gpt":0.2757852147641446,"score_spread":0.2534403842634591,"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."}}