{"id":"W2906175719","doi":"10.4028/www.scientific.net/msf.941.1802","title":"Micro-Nanostructured Silicone Rubber Surfaces Using Compression Molding","year":2018,"lang":"en","type":"article","venue":"Materials science forum","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Contact angle; Silicone rubber; Wetting; Composite material; Compression molding; Molding (decorative); Silicone; Surface roughness; Natural rubber; Surface finish; Hysteresis; Mold","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001250364,0.0002522514,0.0003295207,0.0001692,0.001304825,0.0006966005,0.0008871637,0.0001019884,0.004991909],"category_scores_gemma":[0.0001493968,0.0002043149,0.00004104839,0.000534671,0.001748868,0.0008810666,0.0004167176,0.00005874298,0.0006341116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281287,"about_ca_system_score_gemma":0.0001682847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004576859,"about_ca_topic_score_gemma":0.00001388931,"domain_scores_codex":[0.9971643,0.0001268521,0.0004725884,0.0007453574,0.0006314961,0.0008594111],"domain_scores_gemma":[0.9986463,0.00003149219,0.0002017955,0.0006195543,0.0003048313,0.0001959973],"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":[0.00003701026,0.00002315592,0.0007882451,0.000008247901,0.000001184467,0.000001555103,0.0004548031,0.00008825716,0.9981918,0.0001458072,0.0001991282,0.00006082752],"study_design_scores_gemma":[0.0002515503,0.00006226379,0.001751302,0.00003851463,0.000006330164,0.00002265304,0.0003030407,0.001568645,0.9948421,0.0002400323,0.0006320644,0.0002815227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941791,0.00004536282,0.000243895,0.0001724207,0.004582627,0.0002731237,0.00006424436,0.0001730662,0.0002661526],"genre_scores_gemma":[0.9922884,0.000003513109,0.007131479,0.0002825776,0.0001439095,0.000005951724,0.000004781769,0.00002265026,0.0001167664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006887584,"threshold_uncertainty_score":0.9999954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278306651037792,"score_gpt":0.2874982898105886,"score_spread":0.2596676247068094,"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."}}