{"id":"W1968048419","doi":"10.1016/s0167-8655(02)00099-5","title":"Entropy-based representation of image information","year":2002,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Chromatic scale; Artificial intelligence; Pattern recognition (psychology); Entropy (arrow of time); Computer science; Information theory; Computer vision; Representation (politics); Information loss; Mathematics; Statistics; Combinatorics","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":[0.00006215719,0.00008005815,0.00008542916,0.000102257,0.00002254768,0.00002280458,0.00007331069,0.00004493151,0.000360438],"category_scores_gemma":[0.0000512152,0.00008675263,0.00009809247,0.00009473129,0.0000417352,0.00002393972,0.0000189303,0.00004083304,0.0001071034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009033144,"about_ca_system_score_gemma":0.000002769626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002848835,"about_ca_topic_score_gemma":0.000003011417,"domain_scores_codex":[0.999375,0.00004997087,0.0002308,0.0001280633,0.0001189628,0.00009717746],"domain_scores_gemma":[0.9995005,0.000008847994,0.0001597388,0.0002054415,0.0001010503,0.00002445602],"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":[0.000009880115,0.00003307945,0.002580319,0.00002117019,0.00002304985,0.000001126847,0.00003071337,0.000009654525,0.9173281,1.124003e-7,0.0177408,0.06222207],"study_design_scores_gemma":[0.0003601423,0.0000468997,0.001007958,0.00001268532,0.00002885726,0.000002503638,0.00001788365,0.001246493,0.9949912,0.000009144331,0.002164262,0.0001119204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8165207,0.00001421382,0.1815336,0.0007396034,0.00002161183,0.0001599484,0.00002016142,0.00002959872,0.0009605624],"genre_scores_gemma":[0.9925845,0.00003758072,0.002595692,0.003614248,0.00006069561,0.00002930881,0.001055063,0.000008721618,0.00001413981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1789379,"threshold_uncertainty_score":0.3946544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272054406670451,"score_gpt":0.2409722343706628,"score_spread":0.2282516903039583,"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."}}