{"id":"W2607449659","doi":"10.1016/j.powtec.2017.04.051","title":"Design of trimodal Fe micro-nanopowder feedstock for micro powder injection molding","year":2017,"lang":"en","type":"article","venue":"Powder Technology","topic":"Injection Molding Process and Properties","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Raw material; Materials science; Extrusion; Homogeneity (statistics); Agglomerate; Composite material; Rheometer; Particle size; Particle-size distribution; Plastics extrusion; Rheology; Chemical 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":[0.0001165094,0.0002836099,0.0002699454,0.0002032102,0.0001898622,0.0003223397,0.0003349067,0.0002981238,0.0007517494],"category_scores_gemma":[0.0001380825,0.0001811723,0.0002367352,0.0001805451,0.0001245374,0.0003152526,0.0001553137,0.0002529841,0.000342159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005074273,"about_ca_system_score_gemma":0.0003117649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005610695,"about_ca_topic_score_gemma":0.00170211,"domain_scores_codex":[0.9999218,0.000003669811,0.000005062986,0.00002574995,0.0000291692,0.00001456113],"domain_scores_gemma":[0.9999238,0.000009851551,0.00002728966,0.000008167503,0.00002181422,0.000009029729],"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":[0.00003737692,0.00002256165,0.0001363929,0.00006650828,0.00000449443,0.00002865239,0.00000823405,0.003095488,0.9916115,0.0004672197,0.00005839621,0.004463256],"study_design_scores_gemma":[0.00001318332,0.0001648919,0.0006815816,0.000003853316,0.000008978594,0.00004063314,0.00001169007,0.02388806,0.9731874,0.0001010597,0.001892299,0.000006384981],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8488955,0.000784194,0.1410872,0.0001231664,0.00007247309,0.0001928642,0.000428144,0.0005172229,0.007899252],"genre_scores_gemma":[0.9450417,0.0002791033,0.05222816,0.00001895878,0.000009187437,0.0001188166,0.0001723727,0.00004844772,0.002083237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007517494,"threshold_uncertainty_score":0.00368166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02671147327221964,"score_gpt":0.2519808278670164,"score_spread":0.2252693545947968,"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."}}