{"id":"W4417477756","doi":"10.1007/s44275-025-00035-2","title":"DA: towards distribution adaptive test-time adaptation in dynamic wild world","year":2025,"lang":"en","type":"article","venue":"Moore and More","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Tsinghua University; Shenzhen Technology University","keywords":"Normalization (sociology); Robustness (evolution); Test data; Batch processing; Adaptation (eye); Database normalization; Dynamic data","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002018074,0.001018214,0.00115069,0.0008855973,0.0004664551,0.001013892,0.002763706,0.001110424,0.001368225],"category_scores_gemma":[0.007695079,0.0005484735,0.000732967,0.0008460233,0.001106999,0.002514109,0.002122237,0.002565528,0.0008154002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008573152,"about_ca_system_score_gemma":0.0009768612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006994615,"about_ca_topic_score_gemma":0.004961685,"domain_scores_codex":[0.9989706,0.0003043344,0.00005210551,0.0003699377,0.0001919572,0.000111007],"domain_scores_gemma":[0.9974684,0.001113951,0.0001917622,0.0005318711,0.0005219411,0.0001722087],"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":[0.0003337769,0.0003200428,0.004577914,0.00009644044,0.0001602256,0.0002139276,0.0002111634,0.5407932,0.01909065,0.009190392,0.006072349,0.4189399],"study_design_scores_gemma":[0.000004055078,0.00001294836,0.0002512523,0.000001831239,0.000004903318,0.00002053214,0.000009493627,0.9945182,0.001960608,0.00282488,0.0003844987,0.000006722763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02083128,0.0001457246,0.9761289,0.0001337415,0.00005510265,0.00003320069,0.00007261559,0.002059568,0.0005398718],"genre_scores_gemma":[0.6777113,0.0002166648,0.3168231,0.0004489146,0.0001281322,0.0001502685,0.0006478376,0.0006201126,0.003253698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006994615,"threshold_uncertainty_score":0.01390779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131066449168065,"score_gpt":0.2547205248550893,"score_spread":0.2434098603634087,"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."}}