{"id":"W7021298023","doi":"","title":"New applications of image correlation spectroscopy to reveal mechanisms of cell membrane receptor regulation","year":2017,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Spider Taxonomy and Behavior Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Killam Trusts; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Fluorescence correlation spectroscopy; Colocalization; Fluorescence microscope; Fluorescence; Biomolecule; Microscopy; Measure (data warehouse); Fluorescence spectroscopy; Fluorescence cross-correlation spectroscopy; Spectroscopy","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":[0.0003422948,0.0004498367,0.0005890346,0.0002031161,0.0004568139,0.00002447359,0.0005332323,0.0006327166,0.0001589765],"category_scores_gemma":[0.0001956167,0.0005057447,0.0002947364,0.0001895623,0.00004644753,0.0000333778,0.000105915,0.0003260689,0.0000583995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009913126,"about_ca_system_score_gemma":0.00009529992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000200807,"about_ca_topic_score_gemma":0.0003543906,"domain_scores_codex":[0.9976696,0.00009127522,0.0007697705,0.0007790814,0.0003773898,0.000312907],"domain_scores_gemma":[0.9971559,0.00002493481,0.001152092,0.0009947621,0.0004958916,0.0001764129],"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":[0.0002126528,0.0001134945,0.00002515343,0.000205795,0.00007475367,4.458043e-7,0.000004816677,0.000007619157,0.9781808,0.002417321,0.00008062782,0.01867653],"study_design_scores_gemma":[0.0005266555,0.000356406,0.001180751,0.0001484842,0.0002305152,0.000001588042,0.000110889,7.890756e-7,0.9748671,0.002048299,0.02008707,0.0004414482],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9246351,0.0003801927,0.0008797839,0.00002888533,0.001403721,0.002707134,0.001770843,0.0000623817,0.06813202],"genre_scores_gemma":[0.9302235,0.0002096236,0.03667534,0.00003165831,0.0001216216,0.0002718022,0.003602723,0.0001118365,0.02875189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03938012,"threshold_uncertainty_score":0.9997394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01102832114920643,"score_gpt":0.255652771949778,"score_spread":0.2446244508005715,"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."}}