{"id":"W4387778634","doi":"10.1016/j.eswa.2023.122151","title":"GAF-Net: Graph attention fusion network for multi-view semi-supervised classification","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Artificial intelligence; Embedding; Graph; Machine learning; Graph embedding; Pattern recognition (psychology); Sensor fusion; Data mining; Theoretical computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0005366395,0.0001944045,0.0002185807,0.0001737173,0.0006917511,0.000240526,0.0005453581,0.00009816101,0.00000574608],"category_scores_gemma":[0.00001756008,0.000170227,0.00008884419,0.001612871,0.00004236156,0.0003233696,0.00006329408,0.0001041791,0.0002924501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006037331,"about_ca_system_score_gemma":0.00006007174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003927998,"about_ca_topic_score_gemma":0.0000147689,"domain_scores_codex":[0.9981566,0.0001120751,0.0004207566,0.0006096089,0.0003174753,0.0003834637],"domain_scores_gemma":[0.9984696,0.0001426587,0.000234793,0.0007703219,0.0002372953,0.0001453182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007237672,0.0005341019,0.004286305,0.0005508135,0.0002171599,0.000005318879,0.005893863,0.03386203,0.03224207,0.6658741,0.07762139,0.1788404],"study_design_scores_gemma":[0.0006720505,0.00004428909,0.004763057,0.0001030672,0.000008037101,0.000008344023,0.0006819978,0.7004429,0.00001955476,0.0002051108,0.2927947,0.0002569386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003676965,0.0007873921,0.9935623,0.0008535426,0.0003093679,0.002727763,0.000007564453,0.0008953109,0.0004890604],"genre_scores_gemma":[0.552667,0.0008179686,0.3810764,0.000947726,0.001465633,0.05368477,0.001130126,0.0001581839,0.008052193],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6665809,"threshold_uncertainty_score":0.6941655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06728185403664276,"score_gpt":0.3063780918996103,"score_spread":0.2390962378629676,"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."}}