{"id":"W4405786882","doi":"10.1109/iros58592.2024.10802153","title":"Inline Photometrically Calibrated Hybrid Visual SLAM","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"HORIZON EUROPE Framework Programme","keywords":"Computer science; Artificial intelligence","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":[0.0008160263,0.001342884,0.001183101,0.001220568,0.0006407766,0.002031585,0.002331672,0.001099386,0.005614507],"category_scores_gemma":[0.002513847,0.0007964643,0.0009143449,0.001418169,0.0007097188,0.001952296,0.003893548,0.001564133,0.002763447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006329893,"about_ca_system_score_gemma":0.001408995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004710158,"about_ca_topic_score_gemma":0.00715888,"domain_scores_codex":[0.9979318,0.0002767711,0.00005972241,0.0006096564,0.0008705974,0.0002514995],"domain_scores_gemma":[0.9988353,0.0001253734,0.0001444727,0.0004911119,0.0003379831,0.00006579495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005994289,0.0002668844,0.003589333,0.000356964,0.0002728052,0.0001951816,0.0004300764,0.1735611,0.08564091,0.004860239,0.01431085,0.7159162],"study_design_scores_gemma":[0.000105702,0.0002697265,0.004757697,0.00007219637,0.00006146767,0.0004037206,0.000211558,0.907868,0.05381386,0.008246342,0.0240722,0.0001175276],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02501066,0.0003821851,0.95879,0.00009627682,0.0002236022,0.00007401898,0.0004561747,0.008285489,0.006681627],"genre_scores_gemma":[0.49178,0.0002637959,0.4961227,0.0003257923,0.0001338623,0.0001498722,0.001863594,0.00113182,0.008228576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005614507,"threshold_uncertainty_score":0.01878244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006510264393786953,"score_gpt":0.2245084110520587,"score_spread":0.2179981466582717,"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."}}