{"id":"W6987288200","doi":"","title":"A stochastic computing implementation of the disparity energy model for depth perception","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Stochastic computing; Neuromorphic engineering; Resilience (materials science); Massively parallel; Artificial neural network; Efficient energy use; Reduction (mathematics); Product (mathematics); Energy (signal processing)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009902414,0.000570964,0.0005647264,0.0003098508,0.001128257,0.00009124847,0.001986979,0.0004503271,0.00000947977],"category_scores_gemma":[0.000370469,0.000468064,0.0004767546,0.0004733759,0.0000480636,0.0007973603,0.000420493,0.0005488434,0.000005242023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007314624,"about_ca_system_score_gemma":0.0001459366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006290304,"about_ca_topic_score_gemma":0.005169195,"domain_scores_codex":[0.9962599,0.0002916192,0.0009663133,0.001073032,0.0007867778,0.0006223517],"domain_scores_gemma":[0.9963436,0.0003817494,0.00127739,0.001177207,0.0006914008,0.0001286967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006289525,0.0001149548,0.00006775723,0.0002795694,0.00009461654,9.431188e-7,0.0001259389,0.0009066744,0.06308329,0.1776977,0.00001323105,0.7575524],"study_design_scores_gemma":[0.002489535,0.0005644272,0.008442056,0.002612618,0.0004911462,0.0000398945,0.0007445221,0.3483972,0.2636265,0.3689761,0.0006280793,0.002988],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7946201,0.00006207544,0.1944784,0.00006199177,0.00281939,0.002105187,0.000694862,0.001109186,0.004048758],"genre_scores_gemma":[0.9767301,0.00001323893,0.02187503,0.0001260349,0.0000420186,0.00020908,0.0001397693,0.0001030279,0.0007617077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7545645,"threshold_uncertainty_score":0.9997771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02355920655035312,"score_gpt":0.293518399991455,"score_spread":0.2699591934411019,"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."}}