{"id":"W4386488465","doi":"10.2139/ssrn.4563352","title":"Waterdrop-Assisted Efficient Fog Collection on Micro-Fiber Grids","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fiber; Computer science; Materials science; Composite material","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":[0.0001213331,0.0003179723,0.0004632681,0.000193765,0.0005078919,0.0004891015,0.0005688759,0.0003321502,0.002101425],"category_scores_gemma":[0.0002829528,0.0001386294,0.0002154456,0.000324706,0.0002718828,0.0006296281,0.0007603293,0.0003468314,0.0003936358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003827731,"about_ca_system_score_gemma":0.0004949692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003274441,"about_ca_topic_score_gemma":0.005206818,"domain_scores_codex":[0.9998515,0.0000190437,0.000003331839,0.00002909905,0.00004916606,0.0000478665],"domain_scores_gemma":[0.9998304,0.00004789411,0.00001136313,0.00003153296,0.00005813217,0.00002069103],"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":[0.001684553,0.000472348,0.005055877,0.0003057338,0.0001472612,0.001001037,0.0004488808,0.2552676,0.3773331,0.01360349,0.02000209,0.3246781],"study_design_scores_gemma":[0.000020672,0.0001037356,0.0006396953,0.000005977954,0.00001416285,0.00008489044,0.00008108943,0.9522561,0.041899,0.002512834,0.002366825,0.00001496175],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3845582,0.0007193804,0.5936285,0.0004174155,0.0004266757,0.0001486738,0.0003050777,0.002842998,0.01695308],"genre_scores_gemma":[0.975611,0.0001149556,0.02072603,0.00006141856,0.00002561315,0.00001579201,0.00006238564,0.0000277793,0.003355081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003274441,"threshold_uncertainty_score":0.007029951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182758320093297,"score_gpt":0.2659725343621107,"score_spread":0.2441449511611777,"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."}}