{"id":"W2978693269","doi":"10.22215/etd/2019-13628","title":"Experimental Analysis of Programmable Particles","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Homogeneous; Distributed computing; Simple (philosophy); Physics","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":[0.0006626002,0.000340417,0.0003003144,0.0004192814,0.0006749356,0.0006828835,0.0007687004,0.0007090603,0.008252929],"category_scores_gemma":[0.001583123,0.0001765538,0.0001991898,0.0004668394,0.0008497981,0.0005421891,0.0006443448,0.0007366416,0.001207607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005414864,"about_ca_system_score_gemma":0.0002695316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006787954,"about_ca_topic_score_gemma":0.0003292779,"domain_scores_codex":[0.9994591,0.00005825634,0.00002869249,0.0001604221,0.0001845541,0.0001090101],"domain_scores_gemma":[0.9988776,0.0003751088,0.0001227065,0.0002669501,0.0002461867,0.0001115851],"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":[0.001357868,0.001110297,0.004136885,0.0004464588,0.00007261837,0.0005249242,0.0003599112,0.01179682,0.9355525,0.02151216,0.0036815,0.01944816],"study_design_scores_gemma":[0.0003131058,0.002360885,0.008404584,0.00007041881,0.00006916502,0.0003759219,0.0003059046,0.04212291,0.9217884,0.005888318,0.01822016,0.00008033848],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9494362,0.0009055526,0.02420945,0.0002919389,0.0003047824,0.0001810684,0.001280573,0.0003649041,0.02302559],"genre_scores_gemma":[0.9827198,0.0004061596,0.008265536,0.0001010792,0.00004175654,0.0002307182,0.001405582,0.0001331851,0.006696253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008252929,"threshold_uncertainty_score":0.02760875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02229643253411898,"score_gpt":0.3174123726107749,"score_spread":0.2951159400766559,"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."}}