{"id":"W2994877128","doi":"","title":"The RatCAP front-end electronics","year":2008,"lang":"en","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Brookhaven National Laboratory; Biological and Environmental Research; CMC Microsystems; Natural Sciences and Engineering Research Council of Canada; Stony Brook University; Fonds Québécois de la Recherche sur la Nature et les Technologies; U.S. Department of Energy","keywords":"Electronics; Front and back ends; Front (military); Computer science; Engineering; Electrical engineering; Mechanical engineering","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":[0.0005803646,0.001504285,0.0007590869,0.001057052,0.0003669236,0.001392167,0.002920203,0.0009998579,0.05159192],"category_scores_gemma":[0.0006655447,0.0004855528,0.0004908298,0.0004614532,0.0002408731,0.0008069297,0.0007166513,0.0009090443,0.02712363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006959437,"about_ca_system_score_gemma":0.0008805852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009069424,"about_ca_topic_score_gemma":0.0008732391,"domain_scores_codex":[0.9992944,0.00006166604,0.00002837905,0.0001719418,0.0003507634,0.00009291272],"domain_scores_gemma":[0.9993796,0.00007242218,0.00005841344,0.0001021555,0.0003460738,0.00004124343],"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":[0.001894584,0.0006828043,0.003814354,0.00210637,0.0002337618,0.001139004,0.000317396,0.006414066,0.4084992,0.02257717,0.2343414,0.31798],"study_design_scores_gemma":[0.0002439511,0.003078794,0.003285087,0.0002484343,0.0001722355,0.002152484,0.0001127837,0.04057785,0.4239821,0.001877178,0.5240606,0.0002084315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0943272,0.003321409,0.577268,0.00118141,0.002372356,0.003357385,0.01018397,0.09588186,0.2121065],"genre_scores_gemma":[0.4382603,0.002110246,0.2600155,0.003964982,0.0006238041,0.003033352,0.01002627,0.002497285,0.2794683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05159192,"threshold_uncertainty_score":0.1725922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007527118901508917,"score_gpt":0.1712730471830106,"score_spread":0.1637459282815016,"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."}}