{"id":"W4303980694","doi":"10.3390/rs14194915","title":"Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Science and Technology, Taiwan","keywords":"Computer science; Segmentation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Domain adaptation; Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001108788,0.0002076985,0.0002189001,0.0001378728,0.001351147,0.0002889337,0.0003302435,0.00005523108,0.00001800155],"category_scores_gemma":[0.000100598,0.0002418205,0.0001380734,0.0006275576,0.00004138206,0.000419131,0.0001940309,0.0002449219,0.00001325303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000229722,"about_ca_system_score_gemma":0.00007401373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004888496,"about_ca_topic_score_gemma":0.00001560622,"domain_scores_codex":[0.9976043,0.0004043217,0.000413364,0.0005650329,0.0004996361,0.0005133809],"domain_scores_gemma":[0.9987358,0.0003887344,0.0002355,0.0003928948,0.0001327362,0.0001143716],"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":[0.000111711,0.00002120696,0.00006568631,0.00002241025,0.00003027707,0.00002250567,0.008173032,0.6254754,0.01865735,0.001902795,0.0001988405,0.3453188],"study_design_scores_gemma":[0.001835285,0.0000861067,0.0004172496,0.00002215544,0.000007127625,0.00003716277,0.001290007,0.9817711,0.0004340484,0.005019926,0.008794539,0.0002853544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09789934,0.00007993529,0.8995273,0.0004604892,0.0008187273,0.0006247879,0.000006561746,0.0002878855,0.0002949779],"genre_scores_gemma":[0.3510952,0.000004354874,0.6477938,0.0006379899,0.0001511386,9.383674e-7,0.00007050937,0.00002817392,0.0002178974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3562956,"threshold_uncertainty_score":0.999949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03706489414739324,"score_gpt":0.2763295892066733,"score_spread":0.2392646950592801,"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."}}