{"id":"W6966824045","doi":"10.48436/pcbjd-4wa12","title":"OCID – Object Clutter Indoor Dataset","year":2019,"lang":"en","type":"dataset","venue":"TU Wien Research Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clutter; Object (grammar); Segmentation; Set (abstract data type); Ground truth; Robot; Object detection; Cognitive neuroscience of visual object recognition","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001278817,0.006896607,0.00324615,0.005679764,0.00235525,0.002741531,0.006628963,0.004295554,0.01300471],"category_scores_gemma":[0.003577227,0.00101991,0.003324274,0.006439887,0.00112689,0.002194686,0.003466708,0.002996338,0.02362075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002938191,"about_ca_system_score_gemma":0.002995541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05047994,"about_ca_topic_score_gemma":0.1153906,"domain_scores_codex":[0.9961397,0.0003340949,0.0003846612,0.001079059,0.001514346,0.0005482243],"domain_scores_gemma":[0.9978624,0.0003122806,0.0001764833,0.0007401638,0.0006780798,0.0002305775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006976897,0.0005668219,0.004118296,0.003381017,0.0002895954,0.0005532507,0.0001448273,0.004136783,0.004805683,0.0008800161,0.9309765,0.04944953],"study_design_scores_gemma":[0.0007516653,0.000511156,0.03439227,0.0008202918,0.0002664127,0.002052251,0.0007029838,0.02260569,0.01521471,0.002033417,0.9202763,0.0003729124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01240473,0.002130099,0.003596278,0.000284982,0.0003595622,0.0005757824,0.9592876,0.01561318,0.005747829],"genre_scores_gemma":[0.004695977,0.0001548103,0.004415384,0.00008607053,0.00002295158,0.0002301946,0.9893734,0.0002574966,0.0007637428],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05047994,"threshold_uncertainty_score":0.1003723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4598334532197734,"score_gpt":0.5240239032478986,"score_spread":0.06419045002812523,"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."}}