{"id":"W2157129627","doi":"10.1002/sia.5596","title":"Detection of immunolabels with multi‐isotope imaging mass spectrometry","year":2014,"lang":"en","type":"article","venue":"Surface and Interface Analysis","topic":"Retinal Development and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"National Institute on Aging; National Institutes of Health; Human Frontier Science Program; National Center for Research Resources; Massachusetts Eye and Ear; National Institute of Biomedical Imaging and Bioengineering; Ellison Medical Foundation","keywords":"Synaptophysin; Immunogold labelling; Synaptic vesicle; Chemistry; Mass spectrometry; Antibody; Retina; Isotope; Biophysics; Immunohistochemistry; Biochemistry; Biology; Chromatography; Vesicle; Neuroscience; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001570366,0.0001225746,0.0001925068,0.0001025647,0.00004677968,0.00002250698,0.00008767292,0.00004330865,0.00002157684],"category_scores_gemma":[0.00002389852,0.0001005071,0.00007809695,0.0003665804,0.00006716953,0.00001122244,0.00004182773,0.00005231004,0.000003601849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006055951,"about_ca_system_score_gemma":0.00001089694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007566732,"about_ca_topic_score_gemma":0.00007102151,"domain_scores_codex":[0.9993167,0.00004231444,0.0001565184,0.0002528555,0.00008832748,0.0001433079],"domain_scores_gemma":[0.9995968,0.000008681565,0.00008941182,0.0001937418,0.0000770039,0.00003436784],"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":[0.00007706606,0.00001675325,0.1486054,0.000009758057,0.0004131471,1.483371e-7,0.00003354458,0.0004225065,0.8494216,0.000003933412,0.000008551411,0.0009876202],"study_design_scores_gemma":[0.0004583074,0.0002035332,0.0447408,0.00001133569,0.0002698221,0.000001499585,0.0003560386,0.002276402,0.9502292,0.0000110226,0.001255098,0.0001869083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8612007,0.001154257,0.1368813,0.00004234217,0.00001643825,0.00004873843,0.000001874708,0.000006448043,0.0006478802],"genre_scores_gemma":[0.9952823,0.000335641,0.00379294,0.00002990074,0.000007778926,0.000001690235,0.00001497736,0.00001001638,0.0005247622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1340815,"threshold_uncertainty_score":0.4098562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003603957134155701,"score_gpt":0.2218980259236785,"score_spread":0.2182940687895228,"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."}}