{"id":"W1994336963","doi":"10.1038/srep02190","title":"Collagen morphology and texture analysis: from statistics to classification","year":2013,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":169,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Manitoba; Medical Council of Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Pattern recognition (psychology); Texture (cosmology); Artificial intelligence; Collagen fiber; Gray level; Biomedical engineering; Computer science; Fibrosis; Pathology; Materials science; Anatomy; Medicine; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004618457,0.00009846273,0.0001665719,0.0002197174,0.0002527873,0.0007171029,0.00008526636,0.00006894728,0.005800599],"category_scores_gemma":[0.0001338151,0.00008988153,0.00002744381,0.0007752645,0.00009929916,0.0002459989,0.00005219653,0.00002966603,0.0005638974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003788306,"about_ca_system_score_gemma":0.00006542706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002288915,"about_ca_topic_score_gemma":0.00016634,"domain_scores_codex":[0.9984182,0.00006622096,0.0003862376,0.0006513974,0.0002988289,0.000179187],"domain_scores_gemma":[0.9987454,0.00003474259,0.0002592656,0.000534948,0.0002645043,0.0001611813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000003164579,0.00002319928,0.004195339,0.000002448064,0.00001340515,0.00002253302,0.000405147,0.00003142719,0.970681,0.00005200083,0.02290303,0.001667359],"study_design_scores_gemma":[0.0001293427,0.00003366849,0.8270195,0.000009430651,0.0002517829,0.0000649621,0.000781139,0.003975516,0.1161638,0.004972286,0.0462199,0.0003787662],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751223,0.00001984687,0.02088328,0.0004340324,0.002780485,0.0003266365,0.00005294341,0.00006061693,0.0003198254],"genre_scores_gemma":[0.9915428,0.000002136959,0.003828035,0.0001569682,0.00005699811,0.00005030774,0.0003622588,0.000007525563,0.003992985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8545172,"threshold_uncertainty_score":0.9951082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529269505335832,"score_gpt":0.2550890135086323,"score_spread":0.239796318455274,"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."}}