{"id":"W6931726453","doi":"10.5281/zenodo.8329219","title":"silx-kit/pyFAI: pyFAI-2023.09","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cultural Heritage Materials Analysis","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xenon Pharmaceuticals (Canada)","funders":"","keywords":"Compatibility (geochemistry); Troubleshooting; Selection (genetic algorithm); Generator (circuit theory); Test (biology)","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003617621,0.0003254436,0.0003891337,0.0004419532,0.001737649,0.002404935,0.001099867,0.0001419986,0.5130525],"category_scores_gemma":[0.0002584225,0.0003017313,0.0001585872,0.0002536591,0.0003177303,0.0001746073,0.001059253,0.0002814519,0.144637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001407045,"about_ca_system_score_gemma":0.000002988424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007768674,"about_ca_topic_score_gemma":0.0001897237,"domain_scores_codex":[0.9978527,0.0002522917,0.000345389,0.0005878356,0.0004907223,0.0004709895],"domain_scores_gemma":[0.9986255,0.00001808772,0.0002434069,0.000615297,0.0003349595,0.0001627616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001306112,0.00004731219,1.174952e-7,0.0001224616,0.0001706353,0.00002913122,0.00240819,0.000001641842,0.0001934662,0.01367478,0.9692002,0.01413904],"study_design_scores_gemma":[0.0002147515,0.00008044992,0.000007036897,0.0001249348,0.00007160527,0.00001052429,0.001240387,0.00001662282,0.00002043468,0.000117507,0.9977332,0.0003625333],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001343201,0.0002076551,0.00001803575,0.0004033505,0.0006236308,0.0004047022,0.001710751,0.004713474,0.9917841],"genre_scores_gemma":[0.003885125,0.0003359464,0.00002977267,0.0001006518,0.002186645,1.166147e-7,0.003358538,0.01803053,0.9720727],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3684155,"threshold_uncertainty_score":0.9999435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06185452168792135,"score_gpt":0.2354318636037912,"score_spread":0.1735773419158698,"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."}}