{"id":"W1987958422","doi":"10.1016/j.biomaterials.2011.03.043","title":"Real time responses of fibroblasts to plastically compressed fibrillar collagen hydrogels","year":2011,"lang":"en","type":"article","venue":"Biomaterials","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; National Institutes of Health; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Self-healing hydrogels; Materials science; Extracellular matrix; Biomedical engineering; Biophysics; Scaffold; Tissue engineering; Type I collagen; Confocal microscopy; Cell growth; Cell biology; Chemistry; Biochemistry; Pathology; Polymer chemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003124076,0.0002801976,0.0001865832,0.0001467234,0.0001435845,0.0002899531,0.0001510666,0.0003727086,0.001178345],"category_scores_gemma":[0.0003680736,0.0001467861,0.0001763495,0.000256657,0.0003009648,0.0003524479,0.0001397209,0.0003478954,0.000171575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002369674,"about_ca_system_score_gemma":0.0001428244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008001191,"about_ca_topic_score_gemma":0.001188515,"domain_scores_codex":[0.9998079,0.00003096034,0.00001986508,0.00003970518,0.00005529145,0.00004631137],"domain_scores_gemma":[0.9996129,0.00024901,0.00004122962,0.00002848632,0.00003676275,0.00003162864],"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":[0.0002121552,0.00002211473,0.0001204161,0.00002348501,0.000002370048,0.00005634606,0.000044166,0.00006168288,0.9987613,0.00002241176,0.00001989243,0.0006536442],"study_design_scores_gemma":[0.00001509611,0.0001399366,0.001981957,0.000002279054,0.000005726542,0.00006081934,0.00004650696,0.0008431798,0.9965259,0.00002128073,0.0003512164,0.000006057047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957609,0.000742797,0.002279965,0.00005443657,0.00004588725,0.00002336947,0.0002364923,0.00002097284,0.0008351774],"genre_scores_gemma":[0.9942583,0.0005547764,0.003182752,0.000076431,0.00002871251,0.00004993778,0.0002500926,0.00001341489,0.001585572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001178345,"threshold_uncertainty_score":0.003942013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02904676679045955,"score_gpt":0.2550041038237727,"score_spread":0.2259573370333131,"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."}}