{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024978,0.0003573146,0.0005628674,0.0001742962,0.0002153331,0.0002707484,0.001261789,0.0003020511,0.0001052362],"category_scores_gemma":[0.0003788818,0.0003469881,0.0002392329,0.0001551109,0.000150582,0.0001092867,0.0004150992,0.0006823185,0.00001161253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000666502,"about_ca_system_score_gemma":0.0001186475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000656369,"about_ca_topic_score_gemma":0.001315988,"domain_scores_codex":[0.9974282,0.0007768685,0.0005029369,0.0005314024,0.0004564423,0.0003041568],"domain_scores_gemma":[0.9953478,0.0003946304,0.0003954039,0.002516996,0.001209296,0.000135867],"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":[0.0001724052,0.001871058,0.0460308,0.005264201,0.003386334,0.00007517255,0.03743386,0.8079684,0.0136403,0.004001555,0.007411037,0.07274485],"study_design_scores_gemma":[0.0007991212,0.000001176595,0.005263246,0.0065543,0.0003693736,0.000007468306,0.000160593,0.8804882,0.101421,0.001162613,0.002696016,0.001076938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7529628,0.002214489,0.2143246,0.002251407,0.0001935035,0.0002814066,0.0002585164,0.0004945081,0.02701879],"genre_scores_gemma":[0.9830271,0.001766687,0.01102863,0.00001180094,0.00002197247,0.0000098665,0.0003343807,0.00007158874,0.003727959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2300643,"threshold_uncertainty_score":0.9998982,"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."}}