{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007419893,0.0001027456,0.0001143863,0.000160604,0.000221705,0.0003289759,0.0008641937,0.00004555344,0.00001190022],"category_scores_gemma":[0.0000235827,0.0001028004,0.00002258526,0.00071268,0.00004292622,0.0008844656,0.0002561706,0.0001126811,0.0001699233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001283712,"about_ca_system_score_gemma":0.00003578964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001583838,"about_ca_topic_score_gemma":0.00001618157,"domain_scores_codex":[0.9988616,0.0001599158,0.0001764359,0.0004016299,0.0001882384,0.0002121581],"domain_scores_gemma":[0.9989706,0.0002876025,0.00008137317,0.0005128588,0.00003832426,0.0001092222],"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":[0.000002488758,0.00001992753,0.00004863898,0.00003071841,0.00001650896,0.00001040588,0.001297899,0.0007071904,0.0002247294,0.2433451,0.00318165,0.7511147],"study_design_scores_gemma":[0.000311635,0.0000163575,0.005777767,0.00001142301,0.000005404947,0.00001404535,0.0004576232,0.03934843,0.0001687129,0.0392566,0.9144089,0.000223081],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001669733,0.0008024645,0.9930475,0.00210531,0.0002191575,0.0002061976,0.00001364325,0.0003951302,0.003043608],"genre_scores_gemma":[0.07152759,0.004187191,0.9195951,0.002094002,0.0006307493,0.0007667589,0.0002922367,0.00005512901,0.0008512594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9112273,"threshold_uncertainty_score":0.419208,"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."}}