{"id":"W7073553305","doi":"","title":"Collagen morphology and texture analysis: from statistics to classification","year":2013,"lang":"en","type":"article","venue":"Kobe University Repository Kernel (Kobe University)","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Texture (cosmology); Pattern recognition (psychology); Gray level; Image texture; Co-occurrence matrix; Collagen fiber; Grey level; Principal component analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000731761,0.0001912495,0.00027182,0.0005566027,0.0004900747,0.0001414102,0.0003345025,0.000188723,0.001415037],"category_scores_gemma":[0.00001952858,0.0002379236,0.00007187868,0.0009967011,0.0001297791,0.0005859697,0.0001805903,0.00009267079,0.0002940316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003249941,"about_ca_system_score_gemma":0.0001017991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001969804,"about_ca_topic_score_gemma":0.0003886146,"domain_scores_codex":[0.9985574,0.000214713,0.0001700564,0.0005928008,0.0002200192,0.0002450267],"domain_scores_gemma":[0.9987341,0.00008571489,0.0002108538,0.0003790478,0.0002958302,0.0002944687],"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":[0.0001776776,0.0001165467,0.01547317,0.00001141386,0.0001797067,0.0003214121,0.001239261,0.0002256079,0.9702575,0.002218325,0.009325908,0.0004535113],"study_design_scores_gemma":[0.001321586,0.0001602161,0.8276038,0.00002453585,0.001432855,0.00003009764,0.01119159,0.004489055,0.01361812,0.00006776528,0.1392273,0.000833079],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639238,0.00001071913,0.02735854,0.0003674792,0.0002848948,0.000319679,0.0002199488,0.000142083,0.007372828],"genre_scores_gemma":[0.9538252,0.00002734984,0.002529196,0.0001417487,0.0000754804,3.652117e-7,0.0001347015,0.00001146628,0.04325445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9566393,"threshold_uncertainty_score":0.9994978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00808786232865318,"score_gpt":0.1828307854410628,"score_spread":0.1747429231124096,"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."}}