{"id":"W4412831576","doi":"10.1117/12.3078755","title":"Urban scene segmentation and cross-dataset transfer learning using SegFormer","year":2025,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Transfer of learning; Segmentation; Artificial intelligence; Image segmentation; Transfer (computing); Computer vision; Pattern recognition (psychology)","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":[0.0005323129,0.00004932583,0.00007193036,0.00007279285,0.0008666362,0.0001536524,0.00005435544,0.00004543207,0.0008593148],"category_scores_gemma":[0.00005379756,0.00004794269,0.0000263552,0.0002701497,0.0001823467,0.0002145036,0.000007823681,0.00006791726,0.0000094554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006060931,"about_ca_system_score_gemma":0.0001065954,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006365,"about_ca_topic_score_gemma":0.007890159,"domain_scores_codex":[0.9993626,0.0001124133,0.0001247024,0.000148504,0.0001262958,0.0001254117],"domain_scores_gemma":[0.9997671,0.000069167,0.00001184629,0.00006296916,0.00004762323,0.00004125071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000554143,0.0001983553,0.819122,0.0001482723,0.0002122216,0.000002042884,0.03578571,0.006114828,0.006300998,0.02956443,0.005217572,0.09727811],"study_design_scores_gemma":[0.004488968,0.0001743026,0.1707954,0.0002605045,0.001087267,0.000001055425,0.1251444,0.1432271,0.01781492,0.004556998,0.530648,0.001801129],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.951801,0.00009265801,0.03956642,0.0005840558,0.00004966968,0.0001542298,0.00002985315,0.00004900476,0.007673076],"genre_scores_gemma":[0.9959971,0.0000243364,0.000234127,0.0003110696,0.0000351783,0.000003731651,0.0001580695,0.000002135851,0.003234253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6483266,"threshold_uncertainty_score":0.9965284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02389004085696775,"score_gpt":0.3703959589923683,"score_spread":0.3465059181354005,"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."}}