{"id":"W2170748666","doi":"10.1142/s0129065714500324","title":"Understanding Networks of Computing Chemical Droplet Neurons Based on Information Flow","year":2014,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Nonlinear Dynamics and Pattern Formation","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Information and Communication Technologies; Fundacja na rzecz Nauki Polskiej; European Regional Development Fund; McMaster University","keywords":"Biological system; Microfluidics; Flow (mathematics); Discretization; Computer science; Information flow; Mutual information; Diffusion; Artificial neural network; Function (biology); Work (physics); Mechanics; Nanotechnology; Materials science; Artificial intelligence; Physics; Mathematics; Thermodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002206169,0.0002824038,0.0002879034,0.0004495061,0.0002289424,0.0005675509,0.0006123176,0.0007089333,0.0010783],"category_scores_gemma":[0.001251813,0.0002625743,0.0003458051,0.0002462427,0.0006782047,0.001678278,0.0004433689,0.0003968352,0.00009379249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006920587,"about_ca_system_score_gemma":0.0003092433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002251438,"about_ca_topic_score_gemma":0.001681043,"domain_scores_codex":[0.9999223,0.00001693291,0.000004513176,0.00002385602,0.0000192626,0.00001311435],"domain_scores_gemma":[0.9997229,0.0001437753,0.0000579061,0.00001961554,0.00003191216,0.00002386273],"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":[0.00006662757,0.00003169299,0.001806724,0.00007686403,0.00003286642,0.0001301777,0.0001153208,0.8957244,0.02563519,0.0644995,0.0002749081,0.01160568],"study_design_scores_gemma":[0.000002236054,0.000007110015,0.0002350189,0.000001934966,0.000002564368,0.000009668809,0.000005682173,0.9865997,0.0008446744,0.01217779,0.0001109099,0.000002722486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3217548,0.0006266875,0.672218,0.0005482635,0.00004337239,0.00005148252,0.0001223845,0.0002284689,0.00440647],"genre_scores_gemma":[0.9490277,0.0003537569,0.0488849,0.00005266681,0.0000228107,0.00005992238,0.00006960962,0.00003572891,0.001492933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002251438,"threshold_uncertainty_score":0.005021214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03157244876414224,"score_gpt":0.2460631195900619,"score_spread":0.2144906708259197,"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."}}