{"id":"W7108206087","doi":"10.1016/j.aei.2025.104126","title":"I-FCSAM: An integrated framework of few-shot learning and segment anything model for vision-based indoor built environment management","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Built environment; Development environment; Facility management; Key (lock)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009829707,0.001370006,0.001720761,0.001750089,0.0006273486,0.001545444,0.003585374,0.002200563,0.002436889],"category_scores_gemma":[0.002424139,0.0006251998,0.001886461,0.0009760463,0.0006810195,0.001764252,0.002019841,0.001823076,0.001079974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206379,"about_ca_system_score_gemma":0.001381155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01767765,"about_ca_topic_score_gemma":0.02456246,"domain_scores_codex":[0.9993274,0.00008797988,0.00002875012,0.0003229894,0.0001355798,0.00009738123],"domain_scores_gemma":[0.999511,0.0001763492,0.00004479281,0.00007587347,0.0001213675,0.00007053285],"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":[0.0003378981,0.0003534901,0.00372149,0.0003536241,0.0002969665,0.0003243344,0.0002737798,0.38772,0.01568854,0.008179155,0.01023916,0.5725116],"study_design_scores_gemma":[0.000005024073,0.0000430782,0.0003472722,0.00001485943,0.00001809128,0.00004082672,0.00002654426,0.9926757,0.001819765,0.003549628,0.001444149,0.000015058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01100145,0.0006308408,0.9804136,0.0002256901,0.0001003649,0.0001281383,0.00048556,0.005899746,0.001114603],"genre_scores_gemma":[0.3290844,0.0008005582,0.6588018,0.0008596348,0.0001983491,0.0004632635,0.004493338,0.0007649871,0.004533739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01767765,"threshold_uncertainty_score":0.03514951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006050439110151629,"score_gpt":0.2329125689405831,"score_spread":0.2268621298304314,"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."}}