{"id":"W4406365662","doi":"10.2139/ssrn.5097096","title":"Lifelong Scene Graph Generation","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Graph; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002849534,0.0009960044,0.0006266841,0.001221237,0.000624806,0.00107327,0.001940019,0.001390384,0.04410792],"category_scores_gemma":[0.001594895,0.0006200422,0.001169236,0.0009327813,0.0004012063,0.001335064,0.001939672,0.001344029,0.01146676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006264691,"about_ca_system_score_gemma":0.0007325347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00307505,"about_ca_topic_score_gemma":0.009251508,"domain_scores_codex":[0.9996505,0.00005068658,0.00001107043,0.00012872,0.0001061107,0.00005293289],"domain_scores_gemma":[0.9993754,0.0001248293,0.00001660095,0.0002840969,0.0001500048,0.00004909065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004932085,0.0003666471,0.001458675,0.0004855864,0.0001497542,0.0005338415,0.0002073954,0.06789653,0.03349072,0.03234527,0.1760086,0.6865637],"study_design_scores_gemma":[0.0001402773,0.0001612535,0.0007888715,0.00004572341,0.00007586643,0.0004286186,0.0001467788,0.8187051,0.03159553,0.07001109,0.0778596,0.00004123953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03695937,0.0005363402,0.8842832,0.0008428213,0.0005961518,0.0006467462,0.008317877,0.03698491,0.03083249],"genre_scores_gemma":[0.3082837,0.0003782904,0.6128838,0.0006771829,0.0001765023,0.0005079373,0.0301297,0.007713762,0.03924903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04410792,"threshold_uncertainty_score":0.1475556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371036810080594,"score_gpt":0.2839554494449286,"score_spread":0.2702450813441226,"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."}}