{"id":"W2101103505","doi":"10.1088/1478-3975/2/4/s04","title":"Navigation and analysis of the energy landscape of small proteins using the activation–relaxation technique","year":2005,"lang":"en","type":"article","venue":"Physical Biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Regroupement Québécois sur les Matériaux de Pointe","funders":"","keywords":"Energy landscape; Energy (signal processing); Relaxation (psychology); Energy analysis; Folding (DSP implementation); Topology (electrical circuits); Biological system; Chemical physics; Statistical physics; Protein folding; Resolution (logic); Physics; Chemistry; Computer science; Biology; Mathematics; Thermodynamics; Combinatorics; Artificial intelligence; Biochemistry; Quantum mechanics; Engineering","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":[0.00005396619,0.00005502024,0.0001024613,0.00002305667,0.00003882029,0.000001974774,0.00008870045,0.00007592078,0.000001126498],"category_scores_gemma":[0.00003311691,0.00003066792,0.00006648873,0.0001832041,0.0001115454,0.000001955952,0.00005927735,0.000040298,2.249852e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003968731,"about_ca_system_score_gemma":0.00002056614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005279864,"about_ca_topic_score_gemma":0.00001991702,"domain_scores_codex":[0.9996373,0.00006471774,0.0001005406,0.0001083707,0.00003095589,0.00005810966],"domain_scores_gemma":[0.999613,0.00001605768,0.0001415053,0.0001682302,0.00005058428,0.00001060146],"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":[0.00001578388,0.00001363946,0.003512098,0.000003481735,0.0000954378,5.360363e-9,0.00003269978,0.0006109683,0.9896511,0.00394955,0.000001778308,0.002113399],"study_design_scores_gemma":[0.00007321421,0.00006193398,0.00627207,0.000004328756,0.00009657428,8.432986e-7,0.000009854904,0.0126978,0.9788857,0.001549763,0.0003037977,0.00004409793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9350379,0.00004487353,0.06463475,0.00009615045,0.000006052148,0.0001130401,0.00001383608,0.000002217145,0.00005125295],"genre_scores_gemma":[0.9986705,0.000007114329,0.001100122,0.00005347632,0.00007196531,0.00001690893,0.0000685367,0.000003147996,0.000008253171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06363264,"threshold_uncertainty_score":0.1250602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006930950564015151,"score_gpt":0.2459431780854863,"score_spread":0.2390122275214712,"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."}}