{"id":"W2529914593","doi":"","title":"Posterior sampling of scientific images","year":2014,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Image resolution; Sampling (signal processing); Sample (material); Resolution (logic); Artificial intelligence; Superresolution; Image processing; Image (mathematics); Computer vision; Image registration; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.007221952,0.0008500994,0.001200132,0.002463375,0.0008415474,0.003078014,0.002263948,0.001684836,0.00661697],"category_scores_gemma":[0.02415504,0.001219658,0.001268912,0.001697759,0.003648638,0.003334781,0.002104115,0.003057025,0.001320529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002274304,"about_ca_system_score_gemma":0.001762257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006648944,"about_ca_topic_score_gemma":0.00626096,"domain_scores_codex":[0.9974994,0.0009819234,0.00009134417,0.0006722729,0.0005897268,0.0001653427],"domain_scores_gemma":[0.9876878,0.008634794,0.0008474021,0.001449088,0.001069783,0.0003112086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002798347,0.00009107232,0.006336871,0.0003158806,0.0001892893,0.0002139267,0.0003497092,0.2821951,0.002688688,0.6164479,0.006941956,0.08394979],"study_design_scores_gemma":[0.00003069863,0.00003377432,0.001747224,0.00007344558,0.00003050781,0.0001015946,0.00004587909,0.7894026,0.001242311,0.2036421,0.003617062,0.00003281092],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02656698,0.0007056198,0.9655573,0.001268264,0.0001028097,0.00008810416,0.0006704014,0.000366143,0.004674507],"genre_scores_gemma":[0.7015209,0.002440131,0.2735967,0.0006419937,0.0008200072,0.0004045517,0.003819887,0.0003144967,0.01644128],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.007221952,"threshold_uncertainty_score":0.03819376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04953109065187018,"score_gpt":0.2632681243316868,"score_spread":0.2137370336798167,"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."}}