{"id":"W2006426437","doi":"10.1017/s1431927611006751","title":"A to Z of Technology - Software for Better Results with Faster Sensors","year":2011,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oxford Instruments (Canada)","funders":"","keywords":"Microanalysis; Software; Materials science; Computer science; Engineering; Chemistry; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004883659,0.0001711265,0.0003675107,0.0003094949,0.0001286515,0.00004772049,0.0003203388,0.00008213395,0.000181667],"category_scores_gemma":[0.000172754,0.0001280424,0.00004583362,0.0005172791,0.0002811408,0.00009171492,0.0001305776,0.00005767468,0.00004005491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001317107,"about_ca_system_score_gemma":0.00002705366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000168016,"about_ca_topic_score_gemma":0.00005360602,"domain_scores_codex":[0.9986429,0.00004391019,0.0003554994,0.0005201473,0.0001097069,0.0003278559],"domain_scores_gemma":[0.9990899,0.00005064462,0.0001974789,0.0004116756,0.0001773514,0.00007293792],"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":[0.0003722027,0.00002884955,0.005124636,0.00004247541,0.00002088867,0.000002460311,0.001082721,0.00003647144,0.9924577,0.00001862572,0.0002296684,0.0005832958],"study_design_scores_gemma":[0.0004269073,0.0003054169,0.001380712,0.00004730593,0.0001009479,0.00001410942,0.0001595821,0.00003378648,0.9955614,0.0001158223,0.001670355,0.0001837102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673678,0.00004613481,0.03168618,0.0003373027,0.00006259411,0.0002263547,0.0001447013,0.00005699472,0.00007197901],"genre_scores_gemma":[0.5588123,0.000002708539,0.4405102,0.0002085224,0.00001606927,0.00002716179,0.000006118615,0.00001588397,0.0004009643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4088241,"threshold_uncertainty_score":0.5221418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181458030683319,"score_gpt":0.2560145238242589,"score_spread":0.2441999435174257,"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."}}