{"id":"W2785057641","doi":"10.37236/3797","title":"A Large Set of Torus Obstructions and How They Were Discovered","year":2018,"lang":"en","type":"article","venue":"The Electronic Journal of Combinatorics","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Torus; Embedding; Mathematics; Set (abstract data type); Planar graph; Graph; Book embedding; Quadratic equation; Graph embedding; Combinatorics; Discrete mathematics; Algorithm; Theoretical computer science; 1-planar graph; Computer science; Chordal graph; Artificial intelligence; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002441006,0.00112681,0.00142892,0.003374739,0.004837692,0.004148432,0.001868142,0.002519099,0.008877948],"category_scores_gemma":[0.01357422,0.001324904,0.001834545,0.003319137,0.007000513,0.01274977,0.005742363,0.006648068,0.002056645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002001559,"about_ca_system_score_gemma":0.001644396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002311895,"about_ca_topic_score_gemma":0.002183113,"domain_scores_codex":[0.9971846,0.0005629067,0.0001398101,0.0005909246,0.001016775,0.0005050849],"domain_scores_gemma":[0.9922566,0.003469833,0.0005719017,0.002192343,0.0009708417,0.0005383714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001779213,0.00009774187,0.00391735,0.0008121963,0.00007863958,0.002285883,0.001738705,0.01930048,0.004481629,0.8480867,0.02461231,0.09441052],"study_design_scores_gemma":[0.00002354467,0.0000770482,0.001501394,0.0002653347,0.00005014774,0.002516446,0.001078381,0.02037848,0.004596252,0.8851499,0.08422156,0.0001415423],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1798673,0.01118018,0.6852019,0.01247068,0.002407761,0.000382796,0.002182907,0.00358051,0.102726],"genre_scores_gemma":[0.6225095,0.0138964,0.3255832,0.001853434,0.001092598,0.0003939914,0.003996911,0.001999503,0.0286745],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008877948,"threshold_uncertainty_score":0.02969974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007030571926645761,"score_gpt":0.2144578355214902,"score_spread":0.2074272635948445,"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."}}