{"id":"W3209001167","doi":"10.1016/j.jmbbm.2021.104916","title":"A linear systems model of the hydrothermal isometric tension test for assessing collagenous tissue quality","year":2021,"lang":"en","type":"article","venue":"Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research","keywords":"Isometric exercise; Differential scanning calorimetry; Biological system; Tension (geology); Elasticity (physics); Biophysics; Denaturation (fissile materials); Chemistry; Materials science; Biomedical engineering; Thermodynamics; Composite material; Physics; Compression (physics); Biology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009343281,0.0005967314,0.0030717,0.0005390335,0.0002645271,0.0002000622,0.002173908,0.0009483699,0.001410497],"category_scores_gemma":[0.005751815,0.0003608792,0.001013032,0.001315205,0.0007720542,0.0004849877,0.0007320511,0.0005416586,0.000005166608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000297144,"about_ca_system_score_gemma":0.001480518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005271198,"about_ca_topic_score_gemma":0.000002174962,"domain_scores_codex":[0.9851371,0.001690226,0.007910737,0.000517274,0.004023837,0.0007208187],"domain_scores_gemma":[0.9842554,0.001101031,0.01004726,0.0009435049,0.002969502,0.0006832884],"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":[0.0008642902,0.003719272,0.00003211111,0.0006184793,0.00007323235,0.00008991612,0.00006734201,0.0000120467,0.9930078,0.0002923666,0.0001381137,0.001084997],"study_design_scores_gemma":[0.003104157,0.001767745,0.001190229,0.001366931,0.001454887,0.001501973,0.0002463149,0.0001314736,0.9883716,0.000264304,0.0002448372,0.0003556142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739223,0.0001596141,0.01046991,0.001011758,0.01164606,0.00110395,0.00166555,0.00001914301,0.000001725479],"genre_scores_gemma":[0.9919805,0.000109444,0.00663202,0.0001040951,0.0009611225,0.00003991484,0.00002509664,0.00008839677,0.00005943916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01805818,"threshold_uncertainty_score":0.9998843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04591282874606389,"score_gpt":0.3271713905195884,"score_spread":0.2812585617735245,"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."}}