{"id":"W3023400052","doi":"10.4230/lipics.icalp.2020.110","title":"Sensitive Instances of the Constraint Satisfaction Problem","year":2020,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Constraint satisfaction problem; Idempotence; Tuple; Mathematics; Context (archaeology); Subalgebra; Variety (cybernetics); Consistency (knowledge bases); Discrete mathematics; Combinatorics; Algebra over a field; Pure mathematics","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":[0.0003879596,0.0004860998,0.0006185361,0.0002010185,0.0002890709,0.0003959224,0.001028389,0.0004031434,0.00002119567],"category_scores_gemma":[0.0001233761,0.0003915978,0.0004524382,0.0003879003,0.0003233344,0.0008933954,0.001296868,0.0009577383,0.00002153291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123084,"about_ca_system_score_gemma":0.0005057961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000486896,"about_ca_topic_score_gemma":0.0001364641,"domain_scores_codex":[0.9969325,0.00009927643,0.001442959,0.0004290165,0.0006704547,0.0004257612],"domain_scores_gemma":[0.9966461,0.0001461944,0.001549018,0.000917471,0.0005867039,0.0001545233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003598306,0.0005108645,0.03856234,0.008420506,0.002059639,0.00001506724,0.0925054,0.04280607,0.0008910018,0.3850504,0.009569276,0.4192497],"study_design_scores_gemma":[0.006522017,0.0004181264,0.05247996,0.002871017,0.0003276214,0.0002503382,0.006178326,0.8716562,0.01117363,0.0266973,0.01861033,0.002815171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01996784,0.00002136388,0.9639385,0.002664594,0.002530564,0.002734144,0.001111976,0.0002913685,0.006739689],"genre_scores_gemma":[0.9012043,0.00006352152,0.09736729,0.0009149231,0.00009457943,0.0001000166,0.0001845115,0.0000277717,0.00004305251],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8812365,"threshold_uncertainty_score":0.9998536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01863121969351642,"score_gpt":0.2448525120734376,"score_spread":0.2262212923799212,"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."}}