{"id":"W4312788849","doi":"10.3934/matersci.2022048","title":"Materials for Additive Manufacturing","year":2022,"lang":"en","type":"article","venue":"AIMS Materials Science","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Library science; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009570927,0.0001699192,0.0002039747,0.0001892302,0.0006903752,0.0001682058,0.0007185903,0.00003109839,0.001197824],"category_scores_gemma":[0.0001216372,0.0001695484,0.00002842206,0.0001383619,0.0002476318,0.000212098,0.0004922363,0.00008288711,0.00003968931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002006577,"about_ca_system_score_gemma":0.0000348246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001415992,"about_ca_topic_score_gemma":5.459262e-7,"domain_scores_codex":[0.9985724,0.00002320875,0.0002263667,0.000333269,0.0003445606,0.0005001879],"domain_scores_gemma":[0.9994662,0.00007763507,0.00006221178,0.0003104444,0.00004017479,0.00004328812],"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":[0.00001285288,0.000008870854,8.187288e-7,0.00003596455,0.000008683765,0.000004010902,0.0001219913,0.0008725323,0.9885417,0.00178253,0.002288393,0.006321632],"study_design_scores_gemma":[0.000127582,0.00005384468,0.0003957559,0.000009168184,0.000005024169,0.00001294181,0.000188688,0.0000450187,0.9808521,0.003770747,0.01431547,0.000223612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932179,0.00001279931,0.00152114,0.00006541042,0.002175853,0.0003205479,0.000737508,0.001145047,0.0008038103],"genre_scores_gemma":[0.9973249,0.000008614918,0.001931914,0.00004863947,0.0001077128,0.0004271723,0.0000263672,0.00003131805,0.00009330052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01202708,"threshold_uncertainty_score":0.9997152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138813061608027,"score_gpt":0.227752318966645,"score_spread":0.2138710128058423,"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."}}