{"id":"W4416549096","doi":"10.1016/j.knosys.2025.114942","title":"Context-aware contrastive learning via structural harmony preservation for generalized category discovery","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Graph; Feature learning; Feature (linguistics); Context (archaeology); Representation (politics)","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":[0.0009855316,0.0005965994,0.001454273,0.001353764,0.0007431135,0.001168534,0.002684397,0.001307879,0.002126927],"category_scores_gemma":[0.003883488,0.000459751,0.0008052303,0.001276476,0.001106243,0.003000607,0.003508386,0.001804213,0.0005868565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005269974,"about_ca_system_score_gemma":0.0007659763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002696633,"about_ca_topic_score_gemma":0.004694142,"domain_scores_codex":[0.9992311,0.0001603765,0.00004134954,0.0003531046,0.000140838,0.00007330706],"domain_scores_gemma":[0.9985103,0.0007353959,0.00009919696,0.0003714068,0.0001926484,0.0000911816],"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":[0.0005812808,0.0004351002,0.002177364,0.0002881917,0.0002681783,0.0002129961,0.0003483745,0.09336322,0.043127,0.02578928,0.003950177,0.8294588],"study_design_scores_gemma":[0.00002009761,0.00008908517,0.0006585291,0.00001402807,0.00004906855,0.0001146514,0.00006001018,0.9512897,0.008607994,0.03802752,0.001046234,0.00002304774],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04098246,0.0004829527,0.9561602,0.0001370263,0.00004248308,0.00004612853,0.0001089459,0.0008432046,0.001196427],"genre_scores_gemma":[0.6691171,0.0002946533,0.3271232,0.0002611046,0.00008294593,0.00009364656,0.0005720638,0.0001899388,0.002265357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002696633,"threshold_uncertainty_score":0.007115304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02327946890582423,"score_gpt":0.2818612461954179,"score_spread":0.2585817772895937,"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."}}