{"id":"W4409346510","doi":"10.1609/aaai.v39i2.32228","title":"AoP-SAM: Automation of Prompts for Efficient Segmentation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Automation; Segmentation; Computer science; Artificial intelligence; Computer vision; Engineering","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":[0.001359922,0.001476298,0.0009929007,0.00063566,0.0004793806,0.001246488,0.002370567,0.001586881,0.004589899],"category_scores_gemma":[0.006445265,0.0006132617,0.0009338902,0.0005281634,0.001038934,0.002635309,0.002726306,0.002470113,0.00211893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006858965,"about_ca_system_score_gemma":0.001290507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001806527,"about_ca_topic_score_gemma":0.003679421,"domain_scores_codex":[0.9990655,0.0001987843,0.00004352198,0.0003999065,0.0002024257,0.00008992938],"domain_scores_gemma":[0.9980552,0.000754183,0.0001565311,0.0006585787,0.000244295,0.0001312521],"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":[0.001232336,0.0003086044,0.004492026,0.0004627546,0.00009306625,0.0004425451,0.0008249197,0.1877405,0.08784451,0.02456638,0.02536123,0.6666311],"study_design_scores_gemma":[0.00003781731,0.0001556785,0.000512592,0.00001667709,0.00001495732,0.0001371499,0.00009584671,0.9464899,0.02607097,0.02047843,0.005966428,0.00002358114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01540482,0.0001087391,0.967947,0.0001537439,0.00006088142,0.00008305472,0.0002066608,0.01513674,0.0008984383],"genre_scores_gemma":[0.3166653,0.0001541021,0.6757425,0.0004025865,0.00006629997,0.0002221166,0.001383908,0.002012961,0.003350138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004589899,"threshold_uncertainty_score":0.01535469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03250084120673571,"score_gpt":0.286783357389613,"score_spread":0.2542825161828772,"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."}}