{"id":"W4289516851","doi":"10.20944/preprints202208.0075.v1","title":"Waste Plastic Direct Extrusion Hangprinter","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Compounding; Limiting; Extrusion; Dram; Plastic waste; Fused filament fabrication; Process (computing); Process engineering; Materials science; Computer science; Waste management; Mechanical engineering; Engineering; 3D printing; Composite material; Computer hardware","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":[0.000264157,0.0005320905,0.0003678549,0.000738087,0.0002724153,0.000605578,0.0007185168,0.0006320067,0.01140829],"category_scores_gemma":[0.0004189908,0.0002820747,0.0004576465,0.0005290537,0.0002726894,0.0006602923,0.0007286456,0.0007934765,0.005691428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002559498,"about_ca_system_score_gemma":0.0001642315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001451462,"about_ca_topic_score_gemma":0.0003302849,"domain_scores_codex":[0.9994017,0.00002178907,0.00003320511,0.0001211615,0.0003750696,0.00004701523],"domain_scores_gemma":[0.9995874,0.00008965182,0.00008895754,0.0001263881,0.00008043759,0.00002722307],"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":[0.0001840966,0.00007326648,0.0005314213,0.0002617548,0.00001729842,0.000780185,0.00007663907,0.001028681,0.9339558,0.001867336,0.00196732,0.05925618],"study_design_scores_gemma":[0.000008250504,0.00008460755,0.000683742,0.000009855029,0.000008186345,0.0003649009,0.000009872874,0.001578423,0.9833256,0.0001482526,0.0137673,0.00001099571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5534906,0.002804286,0.3644296,0.0004702512,0.0008937205,0.0003845292,0.00315426,0.01637362,0.05799901],"genre_scores_gemma":[0.7607787,0.001625126,0.1410495,0.0005312127,0.00009584456,0.000206818,0.002624401,0.00141099,0.09167737],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01140829,"threshold_uncertainty_score":0.03816456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07302073166972649,"score_gpt":0.2893143978706447,"score_spread":0.2162936662009182,"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."}}