{"id":"W4399510283","doi":"10.2139/ssrn.4861269","title":"Multi-Scale Modeling of Fog Harvesting Using Thin-Fiber Grids – Towards New Design Rubrics","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rubric; Scale (ratio); Computer science; Fiber; Grid; Distributed computing; Environmental science; Aerospace engineering; Materials science; Engineering; Geology; Geography; Cartography; Mathematics; Geodesy; 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":[0.0002613144,0.0005417149,0.0006376083,0.0002923947,0.0004149818,0.0009398835,0.001252963,0.001488796,0.001487954],"category_scores_gemma":[0.0006546061,0.000447991,0.0007849023,0.0003143789,0.0006283128,0.001034954,0.0006348611,0.0006820871,0.0003409625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000828061,"about_ca_system_score_gemma":0.0007190562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009741466,"about_ca_topic_score_gemma":0.006433684,"domain_scores_codex":[0.999893,0.00002094542,0.000003486279,0.0000219895,0.00003937396,0.00002132099],"domain_scores_gemma":[0.999799,0.00008102302,0.00002665592,0.00002856887,0.00004342669,0.00002143116],"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":[0.00001201517,0.00003126105,0.0002808679,0.00003596862,0.0000116056,0.00004692552,0.0000262936,0.9875514,0.005641986,0.00417796,0.0001787667,0.002005011],"study_design_scores_gemma":[0.000001117605,0.000002817377,0.00005530218,0.000001435453,0.000001115329,0.000003385686,0.000003337076,0.9992059,0.0002383152,0.0003473729,0.0001381755,0.000001787183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2840256,0.001238929,0.6657962,0.0006666165,0.0002090523,0.0001607921,0.0002908346,0.0005837374,0.04702831],"genre_scores_gemma":[0.9400662,0.000798503,0.04865423,0.0001183835,0.00004914105,0.0001403241,0.0001195836,0.0001656512,0.009888013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009741466,"threshold_uncertainty_score":0.01936948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938633856509844,"score_gpt":0.2598736228450596,"score_spread":0.2204872842799612,"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."}}