{"id":"W6982000928","doi":"","title":"Geometrical in-accuracies and tolerances in microassembly","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deformation (meteorology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00103383,0.0000373117,0.0000979082,0.0002458337,0.00008536959,0.00005335329,0.0001065772,0.00002425709,0.00007207556],"category_scores_gemma":[0.0002440553,0.00003418731,0.0000163231,0.0007255381,0.00009574313,0.0001602204,0.00003535284,0.00007074176,0.000005211592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002022824,"about_ca_system_score_gemma":0.00001915041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009016099,"about_ca_topic_score_gemma":0.0170074,"domain_scores_codex":[0.9993432,0.00004271057,0.0001308454,0.0001185088,0.0001422761,0.0002224553],"domain_scores_gemma":[0.9996257,0.0002445305,0.00002321243,0.00005062056,0.000008025356,0.00004794205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009847221,0.00006103759,0.6857354,0.000006152696,0.000003960282,0.00002864038,0.009173895,0.000002729386,0.008520951,0.01336971,0.0003993654,0.2826883],"study_design_scores_gemma":[0.0002022726,0.00001322794,0.9273315,0.00002930679,0.000003202501,5.894684e-7,0.005942126,0.00008804088,0.0003604656,0.005544622,0.06035298,0.0001316665],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9187645,0.0001315413,0.0001026949,0.0006750167,0.00003002676,0.00003283704,0.000001294797,0.000007023176,0.08025509],"genre_scores_gemma":[0.9986585,0.0002087053,0.0007852035,0.00008778946,0.00005049859,0.000001022476,0.000001005671,0.000001707657,0.000205567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2825567,"threshold_uncertainty_score":0.9490528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865634252352288,"score_gpt":0.3276186364766694,"score_spread":0.3089622939531465,"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."}}