{"id":"W4405230415","doi":"10.1061/9780784485736.051","title":"Origami Infrastructure: A Viable Solution to Construction for Challenging Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Materials and Mechanics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Computer science; Construction engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004343243,0.0004730434,0.0002958592,0.0006384825,0.0005968058,0.001555999,0.00072159,0.001268863,0.002177373],"category_scores_gemma":[0.0006872916,0.0003035525,0.000469473,0.000535079,0.0006456883,0.002410529,0.0009013853,0.001251488,0.001003827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006551635,"about_ca_system_score_gemma":0.0006082304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003224507,"about_ca_topic_score_gemma":0.0009932914,"domain_scores_codex":[0.999743,0.00004445268,0.00001557382,0.00004651773,0.000106709,0.00004373113],"domain_scores_gemma":[0.9997734,0.00006622425,0.00006028832,0.00002187782,0.00005204941,0.00002604634],"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":[0.00009280784,0.0001109993,0.001195874,0.004919513,0.00008114553,0.0007810012,0.001088459,0.005248817,0.5860502,0.1132408,0.01115387,0.2760366],"study_design_scores_gemma":[0.00002069596,0.0006175383,0.00141602,0.001006741,0.0001562604,0.001456899,0.001182666,0.008120118,0.2994805,0.02676931,0.6596412,0.0001319725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2014076,0.2226717,0.4220529,0.0117371,0.00304771,0.0004466962,0.001061682,0.001545261,0.1360293],"genre_scores_gemma":[0.6426407,0.1271908,0.1989981,0.003028823,0.0004453027,0.0004302156,0.001050263,0.000228216,0.02598766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002177373,"threshold_uncertainty_score":0.007284105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005765851196253338,"score_gpt":0.2070367426881004,"score_spread":0.2012708914918471,"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."}}