{"id":"W4285345591","doi":"10.33774/chemrxiv-2021-qb2jb","title":"Computing vibrational energy levels by solving linear equations using a tensor method with an imposed rank","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Compute Canada","keywords":"Rank (graph theory); Tensor (intrinsic definition); Basis (linear algebra); Mathematics; Applied mathematics; Tensor product; Orthogonalization; Realization (probability); Basis function; Hamiltonian (control theory); Algorithm; Mathematical analysis; Mathematical optimization; Pure mathematics; Combinatorics; Geometry","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":[0.0008412793,0.0008719138,0.0007752872,0.0006353568,0.0006286072,0.001066526,0.001077001,0.0007355312,0.007648635],"category_scores_gemma":[0.002082755,0.0004370577,0.0008426403,0.0009380776,0.001056833,0.001529926,0.001013389,0.001807735,0.001879925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000533113,"about_ca_system_score_gemma":0.001366681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00372015,"about_ca_topic_score_gemma":0.004349409,"domain_scores_codex":[0.9994911,0.0001525269,0.00003188101,0.0000551482,0.0002217281,0.00004758381],"domain_scores_gemma":[0.9992321,0.0003423845,0.00006648693,0.0001693545,0.00014792,0.00004184067],"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":[0.0001691344,0.0002177486,0.001193956,0.0004810152,0.0001157952,0.0002509423,0.00045285,0.4471751,0.04643789,0.3423175,0.005126845,0.1560613],"study_design_scores_gemma":[0.00002665772,0.00005390685,0.0001185664,0.00001078847,0.00000847412,0.00004556197,0.00004470386,0.9527044,0.00634117,0.03735986,0.003269254,0.00001666189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01977566,0.00007488786,0.9746042,0.0001804454,0.00004068888,0.0000554264,0.000113036,0.0006044069,0.004551206],"genre_scores_gemma":[0.1228164,0.0001922957,0.8703979,0.0000796459,0.00004687659,0.0001635339,0.0002355585,0.0004265076,0.005641269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007648635,"threshold_uncertainty_score":0.02558726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04861634828908681,"score_gpt":0.3155126797991074,"score_spread":0.2668963315100206,"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."}}