{"id":"W3038053023","doi":"10.1002/smll.202000941","title":"High Throughput Screening of Cell Mechanical Response Using a Stretchable 3D Cellular Microarray Platform","year":2020,"lang":"en","type":"article","venue":"Small","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Materials science; Throughput; Nanotechnology; Flexibility (engineering); Substrate (aquarium); Tissue engineering; Cell; Computer science; Biomedical engineering; Chemistry; Engineering; 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":[0.0003118612,0.0006377444,0.0005739926,0.0005324013,0.0002211797,0.0003954078,0.0004762904,0.0006297514,0.002055067],"category_scores_gemma":[0.000256729,0.0002921701,0.0004506834,0.0003491827,0.0001291532,0.0002629709,0.0003238055,0.0004108869,0.001001879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002745408,"about_ca_system_score_gemma":0.0001897657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003351379,"about_ca_topic_score_gemma":0.0009849205,"domain_scores_codex":[0.9995178,0.00005343556,0.0000313387,0.0001206419,0.0002233662,0.00005343724],"domain_scores_gemma":[0.9998215,0.00006447321,0.00003136084,0.00002547805,0.00003361342,0.00002363514],"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":[0.00001880982,0.00002023248,0.00007452367,0.00002293713,0.000003750415,0.00002686024,0.000005496626,0.0002665593,0.9980705,0.00003061632,0.00005740921,0.001402243],"study_design_scores_gemma":[0.000006155307,0.0002129519,0.00132391,0.000002716837,0.00001432504,0.00006850419,0.00001307021,0.006556292,0.9903573,0.00004490995,0.001383763,0.00001616313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7039431,0.001609439,0.2792119,0.0003338844,0.0001751482,0.000762345,0.005618779,0.002671242,0.005674225],"genre_scores_gemma":[0.6764563,0.001834916,0.3077615,0.0002799298,0.00005245055,0.002015632,0.002880164,0.0001235278,0.008595683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002055067,"threshold_uncertainty_score":0.006874919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05687526345221061,"score_gpt":0.2529969900698109,"score_spread":0.1961217266176003,"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."}}