{"id":"W4410423362","doi":"10.1139/cjc-2025-0027","title":"(Coulomb) Local potential energy density–supramolecular energy (LPED–SME) machine learning prediction—a web application to obtain the local SME from simple inputs","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chemistry; Simple (philosophy); Energy (signal processing); Supramolecular chemistry; Coulomb; Electric potential energy; Molecule; Organic chemistry; Quantum mechanics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008255763,0.0002517969,0.0003314709,0.0001305404,0.0005088809,0.0003098917,0.001073329,0.0001882277,0.0007591851],"category_scores_gemma":[0.0003486449,0.0002175286,0.0001136231,0.0003951629,0.0003568137,0.0001503635,0.000128318,0.0004252818,0.00001971155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004149537,"about_ca_system_score_gemma":0.001711298,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02427967,"about_ca_topic_score_gemma":0.007978421,"domain_scores_codex":[0.9977304,0.0002335201,0.0006273717,0.0004219578,0.0004795069,0.0005072535],"domain_scores_gemma":[0.9981499,0.00009801896,0.0003532555,0.0004451697,0.0003144093,0.0006392428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004505944,0.00001317837,0.001167776,0.00001783497,0.00002269774,0.00009289758,0.00008036781,0.1154575,0.8787258,0.0002242629,0.001864137,0.002288432],"study_design_scores_gemma":[0.0006008067,0.00007804186,0.00117654,0.0001155053,0.00008253162,0.0002312076,0.0002065977,0.09952711,0.804266,0.002671934,0.09067075,0.0003730254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4669532,0.000235751,0.529894,0.00181216,0.0004812471,0.00004931688,0.00006157702,0.00003111424,0.0004816382],"genre_scores_gemma":[0.9970224,0.000007503346,0.0006139085,0.001370303,0.0004328531,0.00001073446,0.00004806398,0.00002459603,0.0004696519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5300692,"threshold_uncertainty_score":0.9822177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002360136249086203,"score_gpt":0.193474320802257,"score_spread":0.1911141845531708,"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."}}