{"id":"W2024216557","doi":"10.1007/s10822-012-9568-8","title":"The SAMPL3 blind prediction challenge: transfer energy overview","year":2012,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Solvation; Molecule; Set (abstract data type); Transfer (computing); Field (mathematics); Energy transfer; Energy (signal processing); Path (computing); Computational chemistry; Biphenyl; Chemistry; Statistical physics; Computer science; Chemical physics; Physics; Mathematics; Organic chemistry; Quantum mechanics","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":[0.003130659,0.0002678589,0.0003627516,0.0002325849,0.0002222816,0.0003122094,0.001354674,0.0001023061,0.000005578675],"category_scores_gemma":[0.00007716494,0.0001975282,0.0003681341,0.0005001718,0.00006264803,0.00114104,0.0001775225,0.0003374894,0.000007203168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001143217,"about_ca_system_score_gemma":0.0002779363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002921259,"about_ca_topic_score_gemma":4.410065e-7,"domain_scores_codex":[0.9958904,0.001523138,0.000845958,0.000246216,0.001001952,0.0004923543],"domain_scores_gemma":[0.9972942,0.001167842,0.0003080545,0.00053419,0.00040116,0.0002945277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001607734,0.0004847168,0.00001711309,0.00003032847,0.0004290628,0.0001067007,0.0008143416,0.2034789,0.00246453,0.3372745,0.002419485,0.4523196],"study_design_scores_gemma":[0.003954974,0.002059787,0.002309823,0.0003269859,0.0002148613,0.002240348,0.00002890554,0.7620831,0.01926545,0.1084442,0.09818126,0.0008902922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001937204,0.01091028,0.9831357,0.001533811,0.002140732,0.0001594934,0.000001403267,0.00004518953,0.0001361812],"genre_scores_gemma":[0.5119259,0.001474945,0.4842536,0.0009971086,0.001263835,0.00001375375,0.000001848407,0.00004299315,0.00002591879],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5586042,"threshold_uncertainty_score":0.8054967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06186547229537633,"score_gpt":0.303661812609395,"score_spread":0.2417963403140186,"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."}}