{"id":"W2135722987","doi":"10.3390/s150510465","title":"Towards a Dynamic Clamp for Neurochemical Modalities","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Defense Advanced Research Projects Agency; Washington State University","keywords":"Microfluidics; Nanosensor; Neurochemical; Artificial neural network; Neural cell; Computer science; Biological system; Nanotechnology; Lab-on-a-chip; Interface (matter); Microfluidic chip; Artificial intelligence; Materials science; Chemistry; Neuroscience; Cell; Biology","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":[0.001582789,0.0005029399,0.0008042312,0.0006046197,0.0004951365,0.002886829,0.003074624,0.001770497,0.004276058],"category_scores_gemma":[0.002456788,0.0007866207,0.0005278616,0.0002883985,0.002754853,0.004725464,0.003566453,0.00335061,0.002066487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113286,"about_ca_system_score_gemma":0.0007354431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004517199,"about_ca_topic_score_gemma":0.0005167688,"domain_scores_codex":[0.9992641,0.0001047497,0.00004777623,0.0002570065,0.0002747786,0.00005157651],"domain_scores_gemma":[0.9991566,0.0003183602,0.00005504673,0.0002218462,0.0001620277,0.00008619544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001991491,0.00004735432,0.0002409908,0.0003495145,0.00004140405,0.0001168044,0.0002530033,0.009455391,0.2035855,0.6911462,0.002170979,0.09239371],"study_design_scores_gemma":[0.00007224426,0.0002602779,0.0004665008,0.0003369517,0.00007125491,0.0004908178,0.0001570515,0.1921732,0.1912315,0.4202161,0.1943656,0.0001584225],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002761197,0.001306725,0.9873302,0.0005955918,0.0002818113,0.0000455247,0.00004960107,0.0006931682,0.006936199],"genre_scores_gemma":[0.1608077,0.00292701,0.8224156,0.001618907,0.0003531228,0.0004723431,0.0001494309,0.0003051392,0.01095087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004276058,"threshold_uncertainty_score":0.01430488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0788742668681447,"score_gpt":0.3085898785976752,"score_spread":0.2297156117295305,"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."}}