{"id":"W4413150740","doi":"10.2139/ssrn.5390805","title":"First-Floor-Finder (F3) – A Robust Method Based on Deep Multi-View Feature Fusion for Automated First-Floor Height Estimation of Suburban Buildings","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Feature (linguistics); Artificial intelligence; Computer science; Estimation; Fusion; Computer vision; Pattern recognition (psychology); Engineering; Systems 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008246301,0.0008063462,0.001238845,0.0008393712,0.0006686366,0.0003601514,0.002360937,0.0008422534,0.00001343061],"category_scores_gemma":[0.0009302545,0.0007147574,0.0008321091,0.000921106,0.00005611743,0.0003436764,0.0005134559,0.004161309,0.000005619203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001468983,"about_ca_system_score_gemma":0.003861838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001661874,"about_ca_topic_score_gemma":0.0007254273,"domain_scores_codex":[0.9936444,0.0007972649,0.001062347,0.001260412,0.0008280858,0.002407505],"domain_scores_gemma":[0.9956245,0.0008879562,0.00132549,0.001290528,0.0006913281,0.0001801573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002034702,0.000338911,0.0001690017,0.0009727039,0.0004182244,0.00001354171,0.0003097809,0.881831,0.00003990087,0.009163209,0.0004315737,0.1061086],"study_design_scores_gemma":[0.002191494,0.0004270824,0.001253891,0.00153573,0.0001679003,0.0001290452,0.00002012979,0.9635261,0.0005376412,0.02882317,0.0007489787,0.0006388774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001465356,0.004606719,0.9871415,0.003875764,0.001207439,0.001141401,0.00003252042,0.0004699793,0.00005933645],"genre_scores_gemma":[0.04622216,0.001298253,0.9511907,0.0002821676,0.0002378681,0.0001481693,0.00007121076,0.00007213113,0.0004774164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1054698,"threshold_uncertainty_score":0.9995304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107384885455434,"score_gpt":0.3144375614105809,"score_spread":0.2933637125560266,"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."}}