{"id":"W4289452547","doi":"","title":"Hardness of Minimum Activation Path","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Path (computing); Computer science; Computer network","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":[0.002571363,0.0002708771,0.0004450406,0.000225128,0.00008708674,0.0001341443,0.000479097,0.0004932509,0.000072852],"category_scores_gemma":[0.0005021188,0.0002912986,0.0002057841,0.000290013,0.00005167769,0.0001174444,0.0003170316,0.0005440073,0.00005178121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263645,"about_ca_system_score_gemma":0.0001197193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005325995,"about_ca_topic_score_gemma":0.00006193173,"domain_scores_codex":[0.997027,0.001399627,0.0005888596,0.0003932847,0.0003723209,0.0002188802],"domain_scores_gemma":[0.9965004,0.0005008279,0.000369261,0.001387905,0.001160675,0.00008092911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001831111,0.001508864,0.004694453,0.007953744,0.001271468,0.000007768602,0.03941828,0.05412365,0.457177,0.04553008,0.04924923,0.3388824],"study_design_scores_gemma":[0.001268332,0.00000120329,0.003194616,0.006945338,0.00007429509,0.000007231777,0.0002932267,0.12209,0.8207082,0.0009946618,0.04347876,0.0009440615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.599047,0.0004681913,0.3086142,0.0004571267,0.002408563,0.0009433517,0.00009756023,0.0005520897,0.0874119],"genre_scores_gemma":[0.9942719,0.000110736,0.00222466,0.000008727146,0.00005264271,0.00004395573,0.0001764886,0.0000575259,0.003053401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3952249,"threshold_uncertainty_score":0.9999539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649999086932364,"score_gpt":0.2159352433254176,"score_spread":0.1994352524560939,"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."}}