{"id":"W2006713138","doi":"10.1002/qua.20616","title":"Fuzzy fragment selection strategies, basis set dependence and HF–DFT comparisons in the applications of the ADMA method of macromolecular quantum chemistry","year":2005,"lang":"en","type":"article","venue":"International Journal of Quantum Chemistry","topic":"Molecular spectroscopy and chirality","field":"Chemistry","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Density matrix; Quantum chemistry; Basis (linear algebra); Chemistry; Macromolecule; Basis set; Quantum; Fragment (logic); Selection (genetic algorithm); Molecule; Set (abstract data type); Matrix (chemical analysis); Statistical physics; Computational chemistry; Density functional theory; Algorithm; Mathematics; Quantum mechanics; Physics; Computer science; Organic chemistry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002486407,0.0002143757,0.0004708552,0.0007868223,0.0004006744,0.0004111003,0.0006200073,0.0002830035,0.001258348],"category_scores_gemma":[0.004271689,0.0001004115,0.00019259,0.0004171763,0.000282547,0.0003865703,0.0002836282,0.0003253359,0.0001140123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003643201,"about_ca_system_score_gemma":0.0003680768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001778099,"about_ca_topic_score_gemma":0.002135426,"domain_scores_codex":[0.999561,0.0002411411,0.00001360508,0.00001350067,0.0001432691,0.00002751596],"domain_scores_gemma":[0.9983169,0.001111096,0.00005792909,0.000124252,0.0003460013,0.00004383535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001668928,0.0005335406,0.005051668,0.0003885909,0.0001621144,0.000200579,0.0002771971,0.5814981,0.02208104,0.07727998,0.001501217,0.3093571],"study_design_scores_gemma":[0.00004780263,0.0001513961,0.0009258536,0.00001197473,0.00001610964,0.00002265722,0.00003050075,0.9865851,0.006989013,0.004788849,0.0004198958,0.00001079082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7728836,0.001164447,0.2169094,0.0003350028,0.00005555298,0.0001217957,0.00008768212,0.0002836735,0.008158902],"genre_scores_gemma":[0.9226435,0.0001776609,0.07651536,0.00003847881,0.000008085683,0.00004590504,0.00005338872,0.00002336067,0.0004942174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002486407,"threshold_uncertainty_score":0.01314956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399370350549058,"score_gpt":0.3172118539493904,"score_spread":0.3032181504438998,"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."}}