{"id":"W2796067442","doi":"10.1038/d41586-017-07528-7","title":"The microscope makers","year":2017,"lang":"en","type":"article","venue":"Nature","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of New Brunswick","funders":"","keywords":"Microscopy; Microscope; Computer science; Optics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008447448,0.001438088,0.000749754,0.002370458,0.004400234,0.009909098,0.003069729,0.004796626,0.2347737],"category_scores_gemma":[0.02306045,0.001294725,0.001267305,0.001279766,0.002996746,0.009124913,0.006169922,0.007553512,0.1263392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004146023,"about_ca_system_score_gemma":0.004701084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002605567,"about_ca_topic_score_gemma":0.004687312,"domain_scores_codex":[0.9907606,0.0009295672,0.0003623738,0.001424004,0.005659392,0.0008641274],"domain_scores_gemma":[0.9851568,0.001611932,0.0005928754,0.002368201,0.007169136,0.003101086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001989707,0.00005118179,0.0005059435,0.0003230596,0.00001941863,0.000387424,0.000698775,0.0002251013,0.0109472,0.09996916,0.7631304,0.1235435],"study_design_scores_gemma":[0.00001942721,0.00001784255,0.0002061055,0.00005409994,0.000006656878,0.0001934417,0.0003360044,0.0001043445,0.002483991,0.005924131,0.9906324,0.00002162344],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005091553,0.006603497,0.08823347,0.09960535,0.06125875,0.001000575,0.00241264,0.007244698,0.7285494],"genre_scores_gemma":[0.03776509,0.003950031,0.06006435,0.02567795,0.004217715,0.0007405875,0.001510183,0.003154678,0.8629194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9955997,"threshold_uncertainty_score":0.7853963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004454315343690222,"score_gpt":0.31191523739473,"score_spread":0.3074609220510398,"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."}}