{"id":"W7024802862","doi":"","title":"SR-CACO-2: A dataset for confocal fluorescence microscopy image super-resolution","year":2024,"lang":"en","type":"other","venue":"Espace ÉTS (ETS)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Confocal; Confocal microscopy; Microscopy; Fluorescence microscope; Image processing; Microscope","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":[0.001029748,0.003582013,0.001830907,0.002843602,0.00096493,0.00205079,0.004147783,0.00344827,0.08166161],"category_scores_gemma":[0.003746098,0.001190745,0.001855368,0.002844359,0.000456792,0.001567552,0.002133188,0.001917057,0.1035804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088751,"about_ca_system_score_gemma":0.002255399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01847616,"about_ca_topic_score_gemma":0.043107,"domain_scores_codex":[0.999071,0.0001040757,0.00006653837,0.0002884257,0.0002934431,0.0001763832],"domain_scores_gemma":[0.9990344,0.0002137381,0.00005473086,0.0003325791,0.0002628285,0.0001016335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001735953,0.00004994683,0.0005052082,0.001105528,0.00007407345,0.00006760674,0.00004739824,0.001556007,0.004361833,0.001407474,0.9795506,0.01110066],"study_design_scores_gemma":[0.0004883264,0.00005203179,0.003214088,0.0003896126,0.0001028507,0.0002606444,0.00009267967,0.01177152,0.01303771,0.007787279,0.9626573,0.0001459386],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001136722,0.0003375611,0.004894701,0.0001337187,0.00007950189,0.00008803154,0.9635808,0.02583456,0.003914292],"genre_scores_gemma":[0.002353644,0.0002018796,0.01220883,0.0001001885,0.00001375242,0.0002504055,0.9780751,0.004889934,0.001906174],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08166161,"threshold_uncertainty_score":0.2731853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611838100379893,"score_gpt":0.310566430523189,"score_spread":0.2944480495193901,"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."}}