{"id":"W2014672025","doi":"10.1016/j.biomaterials.2009.12.002","title":"Integration of statistical modeling and high-content microscopy to systematically investigate cell–substrate interactions","year":2010,"lang":"en","type":"article","venue":"Biomaterials","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Substrate (aquarium); Stiffness; Adhesion; Materials science; Context (archaeology); Self-healing hydrogels; Biological system; Cell adhesion; Mesenchymal stem cell; Fibronectin; Biophysics; Microscopy; Nanotechnology; Cell; Biomedical engineering; Chemistry; Cell biology; Composite material; Biochemistry; Biology; Physics; Optics","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":[0.001011368,0.0006370485,0.000723661,0.0007366749,0.0003661242,0.0007363085,0.001119477,0.0009993598,0.000656112],"category_scores_gemma":[0.002243896,0.0004967213,0.0005725076,0.0007127587,0.0006226449,0.001464594,0.0006220808,0.0008372029,0.0001981453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007132795,"about_ca_system_score_gemma":0.001414483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002287783,"about_ca_topic_score_gemma":0.004225933,"domain_scores_codex":[0.9995188,0.0001186481,0.00002842191,0.00005352303,0.0002371161,0.00004348662],"domain_scores_gemma":[0.9975757,0.001532761,0.0002116541,0.0004141572,0.0002130984,0.0000525534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008252491,0.0003983178,0.004779659,0.0003045427,0.0001577471,0.0001399216,0.0001074018,0.680781,0.243838,0.032381,0.0006855667,0.03634438],"study_design_scores_gemma":[0.000001619232,0.000008153092,0.0003243194,8.656838e-7,0.000003318306,0.00001267326,0.000003941315,0.9925874,0.00588199,0.001091383,0.00007864159,0.000005603335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1140182,0.0002009069,0.8836099,0.0001561206,0.00002962212,0.00004782043,0.00009234655,0.0006217777,0.001223363],"genre_scores_gemma":[0.8374209,0.0003918518,0.1609069,0.00009113204,0.0000291253,0.000177452,0.0001689346,0.0002217232,0.0005919619],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002287783,"threshold_uncertainty_score":0.005348682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02595088655008203,"score_gpt":0.2813986250536032,"score_spread":0.2554477385035212,"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."}}