{"id":"W4316660917","doi":"10.1109/access.2023.3237025","title":"Domain Adaptation: Challenges, Methods, Datasets, and Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":181,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Domain (mathematical analysis); Domain adaptation; Adaptation (eye); Machine learning; Artificial intelligence; Data science; Taxonomy (biology); Data mining","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":[0.008997372,0.001355078,0.001277644,0.003299561,0.000888363,0.003102449,0.002628549,0.002388492,0.001604567],"category_scores_gemma":[0.02175491,0.0006705904,0.001093073,0.00561945,0.001265987,0.004443892,0.002486236,0.004407886,0.001411858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297901,"about_ca_system_score_gemma":0.001949967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004701399,"about_ca_topic_score_gemma":0.004050868,"domain_scores_codex":[0.9936216,0.002221089,0.0005934828,0.001472943,0.001899693,0.0001911544],"domain_scores_gemma":[0.987688,0.007069955,0.0004540561,0.002040158,0.0023544,0.000393575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001965083,0.000278001,0.008257613,0.003312622,0.0002041814,0.0001859568,0.0002666029,0.03414369,0.003955388,0.02104206,0.06384943,0.864308],"study_design_scores_gemma":[0.00006432962,0.0002622661,0.01282933,0.002464451,0.0001913863,0.00152469,0.001679357,0.482026,0.01412746,0.1459279,0.3386563,0.0002465804],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02952328,0.1339537,0.7956355,0.01397403,0.002124224,0.0007560918,0.006144191,0.003373951,0.01451496],"genre_scores_gemma":[0.1673698,0.1085607,0.6862684,0.004383321,0.003108081,0.001664663,0.02147526,0.0007461647,0.006423701],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.008997372,"threshold_uncertainty_score":0.04758316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1339129318727663,"score_gpt":0.399230176361959,"score_spread":0.2653172444891926,"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."}}