{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005480587,0.001211287,0.001375487,0.001322793,0.001183204,0.00141146,0.002699167,0.002576223,0.008155917],"category_scores_gemma":[0.009263715,0.0004427723,0.0009803519,0.001971481,0.0008093744,0.002315614,0.002759621,0.0027485,0.00331762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006250925,"about_ca_system_score_gemma":0.002205097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002702902,"about_ca_topic_score_gemma":0.003337959,"domain_scores_codex":[0.9980798,0.000693795,0.00007603503,0.0002594244,0.0007406682,0.0001502906],"domain_scores_gemma":[0.9972196,0.001656343,0.00008009613,0.0004800125,0.0004593397,0.0001046278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001068704,0.0006026931,0.001585208,0.001343006,0.0003046379,0.0002990544,0.00007924174,0.2202723,0.0123809,0.05037599,0.07788837,0.6337999],"study_design_scores_gemma":[0.0001197039,0.0003203146,0.0007446287,0.0000854197,0.00004654857,0.0002763075,0.00005626579,0.8115391,0.01670141,0.1456813,0.02435218,0.00007682749],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04672738,0.02062768,0.8757505,0.01412314,0.0009713772,0.0003605583,0.007616329,0.008430612,0.02539245],"genre_scores_gemma":[0.4974159,0.01289271,0.4485104,0.003984565,0.00137885,0.0006716538,0.01605266,0.001872078,0.01722115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008155917,"threshold_uncertainty_score":0.02898449,"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."}}