{"id":"W2612525245","doi":"","title":"Shrec'17 Track: Retrieval of surfaces with similar relief patterns","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Task (project management); CONTEST; Track (disk drive); Information retrieval; Texture (cosmology); Characterization (materials science); Artificial intelligence; Pattern recognition (psychology); Data mining; Image (mathematics); 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":[0.002044832,0.003331379,0.00477858,0.00291529,0.0015599,0.003704533,0.004293642,0.004472658,0.05166921],"category_scores_gemma":[0.002799718,0.0008339869,0.002262535,0.00274289,0.001069563,0.002725723,0.002796505,0.001970124,0.03019835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111652,"about_ca_system_score_gemma":0.001966475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01014993,"about_ca_topic_score_gemma":0.0148397,"domain_scores_codex":[0.9979248,0.0002260667,0.00005849384,0.0004550714,0.001088269,0.0002472223],"domain_scores_gemma":[0.9981318,0.0003033041,0.00004017289,0.000700452,0.0006378573,0.0001863756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001686285,0.0005181743,0.0006565471,0.00125173,0.0004591529,0.0003732582,0.0001338531,0.004263422,0.05692212,0.002447341,0.7020065,0.2292817],"study_design_scores_gemma":[0.002579481,0.002528661,0.008814014,0.000208934,0.0005005792,0.002052877,0.0005926341,0.3122384,0.1317253,0.01354819,0.52497,0.0002409782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1393729,0.01540054,0.3864374,0.004887007,0.01297297,0.004034227,0.1823117,0.1610538,0.09352954],"genre_scores_gemma":[0.09750885,0.002214858,0.3461327,0.001743401,0.001401302,0.001306392,0.4309552,0.008775018,0.1099623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05166921,"threshold_uncertainty_score":0.1728507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429874787261289,"score_gpt":0.218209415861279,"score_spread":0.2039106679886661,"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."}}