{"id":"W2187808916","doi":"","title":"Perceptual hashing-based movement compensation applied to in vivo two-photon microscopy","year":2014,"lang":"en","type":"article","venue":"Corpus Université Laval (Université Laval)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Hash function; Computer science; Displacement (psychology); Perception; Microscopy; Movement (music); Optics; Pattern recognition (psychology); Physics; Acoustics; Biology; Psychology; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001961545,0.0003502905,0.0003094804,0.0003251834,0.0002439964,0.00003602869,0.0006090177,0.0002136394,0.00003487277],"category_scores_gemma":[0.00002359024,0.0004388196,0.0001202346,0.0003926607,0.0001219051,0.00002304466,0.0003455743,0.0002097079,0.00001585703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004859301,"about_ca_system_score_gemma":0.0001225483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00161634,"about_ca_topic_score_gemma":0.005662459,"domain_scores_codex":[0.9981993,0.00009068711,0.0002088808,0.0007568914,0.000251548,0.0004927363],"domain_scores_gemma":[0.9989195,0.00002743371,0.0001363084,0.0005728605,0.0001275451,0.0002163258],"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":[0.0005216424,0.0001065261,0.006356752,0.00001682112,0.00001896898,0.00001722262,0.0009164785,0.001260497,0.9862178,0.001352715,0.0008755555,0.00233904],"study_design_scores_gemma":[0.001979417,0.0004566843,0.002139803,0.00005019578,0.00002896239,0.00000283143,0.0007095055,0.0004009437,0.9569181,0.00008053183,0.03678042,0.0004526459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299273,0.00005635087,0.06340867,0.0004338407,0.0001289456,0.0007261466,0.00004903039,0.0001310645,0.005138597],"genre_scores_gemma":[0.9783772,0.00008914682,0.01871112,0.001108025,0.00006937578,0.000007061732,0.0001529745,0.00005693852,0.001428212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04844979,"threshold_uncertainty_score":0.9998063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00540984610710784,"score_gpt":0.2181467229258725,"score_spread":0.2127368768187647,"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."}}