{"id":"W6911766736","doi":"10.5281/zenodo.12752490","title":"(Coulomb) LPED-SME Machine Learning Prediction","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intermolecular force; Boosting (machine learning); Atoms in molecules; RGB color model; Coulomb; Energy (signal processing); Quantum chemical; Set (abstract data type)","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002776508,0.0004294541,0.0004522269,0.0005623338,0.004169412,0.00250396,0.003260004,0.0002953211,0.07264084],"category_scores_gemma":[0.003378555,0.0004409292,0.0001017804,0.0007783931,0.0003617012,0.0003932663,0.003607621,0.00113886,0.01207466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003193521,"about_ca_system_score_gemma":0.00002189886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002676924,"about_ca_topic_score_gemma":0.000002177455,"domain_scores_codex":[0.9949226,0.001489187,0.0006481042,0.001175399,0.001012522,0.0007521503],"domain_scores_gemma":[0.9974163,0.00008636796,0.0004659887,0.001179224,0.0006085312,0.0002436272],"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":[0.00005639886,0.00009442165,0.000001155781,0.0003133682,0.00001703341,0.00001911145,0.00009743393,0.001283705,0.01374963,0.00009768474,0.9820018,0.002268297],"study_design_scores_gemma":[0.0003482105,0.0002870781,0.00004279917,0.0001593871,0.00004996293,0.000095459,0.00003049685,0.001411501,0.0005886783,0.0000491679,0.9965748,0.0003624365],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007202254,0.00014056,0.001203953,0.0003596326,0.001330993,0.0006434053,0.9827181,0.001803862,0.01107933],"genre_scores_gemma":[0.0007304965,0.0002630596,0.0004149263,0.0001752723,0.0004532856,2.079019e-7,0.9945334,0.0006804357,0.002748981],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06056618,"threshold_uncertainty_score":0.9998043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971154438980743,"score_gpt":0.2567455971214708,"score_spread":0.2370340527316634,"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."}}