{"id":"W1987766637","doi":"10.1007/s10439-005-2346-1","title":"Prediction of Seizure Onset in an In-Vitro Hippocampal Slice Model of Epilepsy Using Gaussian-Based and Wavelet-Based Artificial Neural Networks","year":2005,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Ictal; Artificial neural network; Pattern recognition (psychology); Computer science; Wavelet; Artificial intelligence; Gaussian; Hippocampal formation; Local field potential; Electroencephalography; Speech recognition; Neuroscience; Psychology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003593592,0.0001425688,0.0003015237,0.0003704516,0.00001547734,0.00001015567,0.0001629965,0.0001326763,0.000003715191],"category_scores_gemma":[0.000140167,0.0001387102,0.00005221572,0.0004145814,0.0001511256,0.0001476953,0.00002983134,0.0002249248,7.205188e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001330825,"about_ca_system_score_gemma":0.00004692587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002706912,"about_ca_topic_score_gemma":0.000005546637,"domain_scores_codex":[0.9985697,0.00006441135,0.0005533377,0.0002568159,0.0002717531,0.0002840252],"domain_scores_gemma":[0.9993693,0.0002175581,0.0001168167,0.0001499516,0.00003008737,0.0001162613],"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":[0.000092636,0.0001191764,0.000157913,0.00006264285,0.000001656023,0.000003925118,0.00008704323,0.5413364,0.4524765,0.00001269751,0.000005229942,0.005644141],"study_design_scores_gemma":[0.0002665193,0.0001149596,0.0008579605,0.0001138381,0.000002792482,0.000002469765,0.000007955941,0.6932209,0.305333,0.000009203591,0.000002950527,0.00006741008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9404817,0.00004272179,0.05886063,0.0003417649,0.00008625277,0.00009776447,0.00006381638,0.00002307813,0.000002315491],"genre_scores_gemma":[0.9960259,0.000003956,0.003652849,0.0002171033,0.00007414027,0.000002842884,0.000008854207,0.00001398988,3.681007e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1518845,"threshold_uncertainty_score":0.5656438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06974232005869377,"score_gpt":0.2904461650900761,"score_spread":0.2207038450313823,"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."}}