{"id":"W4378447451","doi":"10.2352/issn.2169-4451.2009.25.1.art00102_1","title":"Quantization Frequencies in AM Screens","year":2009,"lang":"en","type":"article","venue":"Technical programs and proceedings/Technical program and proceedings","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Offset (computer science); Computer science; Filter (signal processing); Artificial intelligence; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000420122,0.0003773542,0.0002662846,0.00137458,0.0003273005,0.001185887,0.0004868486,0.0006264467,0.003856285],"category_scores_gemma":[0.00491541,0.0002954105,0.0002497235,0.0007645885,0.0008031115,0.001046748,0.0003923624,0.0004889516,0.0007142145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005931437,"about_ca_system_score_gemma":0.0002205136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001252598,"about_ca_topic_score_gemma":0.0008039044,"domain_scores_codex":[0.999207,0.00009853351,0.00003443688,0.0001148401,0.0004840928,0.00006115209],"domain_scores_gemma":[0.9973641,0.001701062,0.0002718086,0.000189492,0.0004159906,0.00005756011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005999879,0.0001112585,0.009581095,0.0004718287,0.00003337883,0.001052266,0.0008854762,0.09343554,0.3528005,0.1577937,0.003953811,0.3792812],"study_design_scores_gemma":[0.00004903993,0.0003004842,0.01348378,0.00009058547,0.000036621,0.001194363,0.0002428427,0.7099729,0.1908538,0.07111761,0.01253868,0.0001193738],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1889288,0.0006848985,0.7893739,0.0001810154,0.000119627,0.00004771948,0.0001242942,0.001665803,0.01887404],"genre_scores_gemma":[0.9049442,0.0002191223,0.09094048,0.00005855342,0.00002669538,0.00003250732,0.00009572655,0.0001624365,0.003520311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003856285,"threshold_uncertainty_score":0.01290053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01825248905289438,"score_gpt":0.2472670686073824,"score_spread":0.229014579554488,"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."}}