{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001891274,0.0005702855,0.0007239684,0.003321351,0.0001881441,0.001814501,0.0004366814,0.0006271893,0.0006122916],"category_scores_gemma":[0.004527265,0.00026069,0.0004555419,0.002320203,0.001208912,0.001112046,0.0003855922,0.0006038083,0.0003907449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004751909,"about_ca_system_score_gemma":0.0004944844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001031367,"about_ca_topic_score_gemma":0.000773176,"domain_scores_codex":[0.9991014,0.0002020518,0.00007514602,0.0001712392,0.0003943063,0.00005584315],"domain_scores_gemma":[0.9967486,0.001798938,0.0006064685,0.0003256713,0.0004169353,0.000103411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002836461,0.0001409824,0.0278146,0.0004761658,0.0001737345,0.0002204244,0.0003070601,0.03633218,0.1196021,0.01835136,0.00239506,0.7939027],"study_design_scores_gemma":[0.00003890859,0.0004455376,0.06473075,0.0001651436,0.0001526068,0.0012971,0.000423968,0.7882589,0.07956157,0.05440835,0.01034319,0.0001739439],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06432302,0.003481255,0.928187,0.0008031168,0.00009032332,0.00005109038,0.0003185098,0.0008381648,0.001907466],"genre_scores_gemma":[0.6467679,0.004055996,0.346193,0.0002330662,0.0004455246,0.0001285702,0.0004509155,0.0001906085,0.001534436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003321351,"threshold_uncertainty_score":0.01000214,"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."}}